This paper is in the following e-collection/theme issue:

Decision Support for Health Professionals (1210) Electronic Health Records (1068)

Published on in Vol 4, No 3 (2016): Jul-Sept

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach

Authors of this article:

Thomas Desautels1 Author Orcid Image ;   Jacob Calvert1 Author Orcid Image ;   Jana Hoffman1 Author Orcid Image ;   Melissa Jay1 Author Orcid Image ;   Yaniv Kerem2, 3 Author Orcid Image ;   Lisa Shieh4 Author Orcid Image ;   David Shimabukuro5 Author Orcid Image ;   Uli Chettipally6, 7 Author Orcid Image ;   Mitchell D Feldman8 Author Orcid Image ;   Chris Barton7 Author Orcid Image ;   David J Wales9 Author Orcid Image ;   Ritankar Das1 Author Orcid Image
Article Authors Cited by (320) Tweetations (8) Metrics

Journals

  1. Hashimoto D, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology. Anesthesiology 2020;132(2):379 View
  2. Fleuren L, Klausch T, Zwager C, Schoonmade L, Guo T, Roggeveen L, Swart E, Girbes A, Thoral P, Ercole A, Hoogendoorn M, Elbers P. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine 2020;46(3):383 View
  3. Cinel I, Kasapoglu U, Gul F, Dellinger R. The initial resuscitation of septic shock. Journal of Critical Care 2020;57:108 View
  4. Calvert J, Saber N, Hoffman J, Das R. Machine-Learning-Based Laboratory Developed Test for the Diagnosis of Sepsis in High-Risk Patients. Diagnostics 2019;9(1):20 View
  5. Amland R, Burghart M, Overhage J. Sepsis surveillance: an examination of parameter sensitivity and alert reliability. JAMIA Open 2019;2(3):339 View
  6. Zheng L, Lin F, Zhu C, Liu G, Wu X, Wu Z, Zheng J, Xia H, Cai Y, Liang H. Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections. BioMed Research International 2020;2020:1 View
  7. McCoy A, Das R. Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Quality 2017;6(2):e000158 View
  8. Park H, Jung D, Ji W, Choi C. Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study. Journal of Medical Internet Research 2020;22(8):e19512 View
  9. Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Quality & Safety 2019;28(3):231 View
  10. Scherpf M, Gräßer F, Malberg H, Zaunseder S. Predicting sepsis with a recurrent neural network using the MIMIC III database. Computers in Biology and Medicine 2019;113:103395 View
  11. Li X, Xu X, Xie F, Xu X, Sun Y, Liu X, Jia X, Kang Y, Xie L, Wang F, Xie G. A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care. Critical Care Medicine 2020;48(10):e884 View
  12. Piccolo S, Lee T, Suh E, Hill K. ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data. GigaScience 2020;9(4) View
  13. Tang S, Chappell G, Mazzoli A, Tewari M, Choi S, Wiens J. Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records. JCO Clinical Cancer Informatics 2020;(4):128 View
  14. Medic G, Kosaner Kließ M, Atallah L, Weichert J, Panda S, Postma M, EL-Kerdi A. Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review. F1000Research 2019;8:1728 View
  15. Dummitt B, Zeringue A, Palagiri A, Veremakis C, Burch B, Yount B. Using survival analysis to predict septic shock onset in ICU patients. Journal of Critical Care 2018;48:339 View
  16. Baldassano S, Roberson S, Balu R, Scheid B, Bernabei J, Pathmanathan J, Oommen B, Leri D, Echauz J, Gelfand M, Bhalla P, Hill C, Christini A, Wagenaar J, Litt B. IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit. IEEE Journal of Biomedical and Health Informatics 2020;24(8):2389 View
  17. Sendak M, Ratliff W, Sarro D, Alderton E, Futoma J, Gao M, Nichols M, Revoir M, Yashar F, Miller C, Kester K, Sandhu S, Corey K, Brajer N, Tan C, Lin A, Brown T, Engelbosch S, Anstrom K, Elish M, Heller K, Donohoe R, Theiling J, Poon E, Balu S, Bedoya A, O'Brien C. Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study. JMIR Medical Informatics 2020;8(7):e15182 View
  18. Schinkel M, Paranjape K, Nannan Panday R, Skyttberg N, Nanayakkara P. Clinical applications of artificial intelligence in sepsis: A narrative review. Computers in Biology and Medicine 2019;115:103488 View
  19. Gupta A, Liu T, Shepherd S. Clinical decision support system to assess the risk of sepsis using Tree Augmented Bayesian networks and electronic medical record data. Health Informatics Journal 2020;26(2):841 View
  20. Yee C, Narain N, Akmaev V, Vemulapalli V. A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit. Biomedical Informatics Insights 2019;11 View
  21. Tadesse G, Javed H, Thanh N, Thi H, Tan L, Thwaites L, Clifton D, Zhu T. Multi-Modal Diagnosis of Infectious Diseases in the Developing World. IEEE Journal of Biomedical and Health Informatics 2020;24(7):2131 View
  22. Bates D, Auerbach A, Schulam P, Wright A, Saria S. Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence. Annals of Internal Medicine 2020;172(11_Supplement):S137 View
  23. Das R, Wales D. Machine learning landscapes and predictions for patient outcomes. Royal Society Open Science 2017;4(7):170175 View
  24. Ibrahim Z, Wu H, Hamoud A, Stappen L, Dobson R, Agarossi A. On classifying sepsis heterogeneity in the ICU: insight using machine learning. Journal of the American Medical Informatics Association 2020;27(3):437 View
  25. Sinha M, Jupe J, Mack H, Coleman T, Lawrence S, Fraley S. Emerging Technologies for Molecular Diagnosis of Sepsis. Clinical Microbiology Reviews 2018;31(2) View
  26. Vázquez-López R, Rivero Rojas O, Ibarra Moreno A, Urrutia Favila J, Peña Barreto A, Ortega Ortuño G, Abello Vaamonde J, Aguilar Velazco I, Félix Castro J, Solano-Gálvez S, Barrientos Fortes T, González-Barrios J. Antibiotic-Resistant Septicemia in Pediatric Oncology Patients Associated with Post-Therapeutic Neutropenic Fever. Antibiotics 2019;8(3):106 View
  27. Khalifa M, Magrabi F, Gallego B. Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support. BMC Medical Informatics and Decision Making 2019;19(1) View
  28. Hong W, Haimovich A, Taylor R, Dong Q. Predicting hospital admission at emergency department triage using machine learning. PLOS ONE 2018;13(7):e0201016 View
  29. Desautels T, Calvert J, Hoffman J, Mao Q, Jay M, Fletcher G, Barton C, Chettipally U, Kerem Y, Das R. Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting. Biomedical Informatics Insights 2017;9:117822261771299 View
  30. Barton C, Chettipally U, Zhou Y, Jiang Z, Lynn-Palevsky A, Le S, Calvert J, Das R. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs. Computers in Biology and Medicine 2019;109:79 View
  31. Pruinelli L, Westra B, Yadav P, Hoff A, Steinbach M, Kumar V, Delaney C, Simon G. Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock*. Critical Care Medicine 2018;46(4):500 View
  32. Choi J, Trinh T, Ha J, Yang M, Lee Y, Kim Y, Choi J, Byun H, Song J, Yoon T. Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever. Scientific Reports 2020;10(1) View
  33. Shashikumar S, Stanley M, Sadiq I, Li Q, Holder A, Clifford G, Nemati S. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of Electrocardiology 2017;50(6):739 View
  34. Jouffroy R, Saade A, Ellouze S, Carpentier A, Michaloux M, Carli P, Vivien B. Prehospital triage of septic patients at the SAMU regulation: Comparison of qSOFA, MRST, MEWS and PRESEP scores. The American Journal of Emergency Medicine 2018;36(5):820 View
  35. Woods J, Saxena M, Nagamine T, Howell R, Criscitelli T, Gorenstein S, M. Gillette B. The Future of Data‐Driven Wound Care. AORN Journal 2018;107(4):455 View
  36. Luz C, Vollmer M, Decruyenaere J, Nijsten M, Glasner C, Sinha B. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clinical Microbiology and Infection 2020;26(10):1291 View
  37. Lee D, Yetisgen M, Vanderwende L, Horvitz E. Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision. Journal of Biomedical Informatics 2020;107:103425 View
  38. Wolff J, Gary A, Jung D, Normann C, Kaier K, Binder H, Domschke K, Klimke A, Franz M. Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach. BMC Medical Informatics and Decision Making 2020;20(1) View
  39. Lee J. Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes?. Journal of Medical Internet Research 2020;22(8):e19918 View
  40. Nemati S, Holder A, Razmi F, Stanley M, Clifford G, Buchman T. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine 2018;46(4):547 View
  41. Meiring C, Dixit A, Harris S, MacCallum N, Brealey D, Watkinson P, Jones A, Ashworth S, Beale R, Brett S, Singer M, Ercole A, Celi L. Optimal intensive care outcome prediction over time using machine learning. PLOS ONE 2018;13(11):e0206862 View
  42. Núñez Reiz A, Armengol de la Hoz M, Sánchez García M. Big Data Analysis y Machine Learning en medicina intensiva. Medicina Intensiva 2019;43(7):416 View
  43. Kulabukhov V, Kudryavtsev A, Kleuzovich A, Chizhov A, Raevskaya M. Diagnostic value of molecular biomarkers of infection in screening by Sepsis-3 criteria. Khirurgiya. Zhurnal im. N.I. Pirogova 2018;(5):58 View
  44. Kapoor R, Walters S, Al-Aswad L. The current state of artificial intelligence in ophthalmology. Survey of Ophthalmology 2019;64(2):233 View
  45. Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber N, Das R. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data. Canadian Journal of Kidney Health and Disease 2018;5 View
  46. Lee H, Jung C. Anesthesia research in the artificial intelligence era. Anesthesia and Pain Medicine 2018;13(3):248 View
  47. Kok C, Jahmunah V, Oh S, Zhou X, Gururajan R, Tao X, Cheong K, Gururajan R, Molinari F, Acharya U. Automated prediction of sepsis using temporal convolutional network. Computers in Biology and Medicine 2020;127:103957 View
  48. Núñez Reiz A, Armengol de la Hoz M, Sánchez García M. Big Data Analysis and Machine Learning in Intensive Care Units. Medicina Intensiva (English Edition) 2019;43(7):416 View
  49. Saber H, Somai M, Rajah G, Scalzo F, Liebeskind D. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurological Research 2019;41(8):681 View
  50. Kasson P. Infectious Disease Research in the Era of Big Data. Annual Review of Biomedical Data Science 2020;3(1):43 View
  51. Dai Z, Liu S, Wu J, Li M, Liu J, Li K, Beiki O. Analysis of adult disease characteristics and mortality on MIMIC-III. PLOS ONE 2020;15(4):e0232176 View
  52. López-Martínez F, Núñez-Valdez E, Lorduy Gomez J, García-Díaz V. A neural network approach to predict early neonatal sepsis. Computers & Electrical Engineering 2019;76:379 View
  53. Medic G, Kosaner Kließ M, Atallah L, Weichert J, Panda S, Postma M, EL-Kerdi A. Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review. F1000Research 2019;8:1728 View
  54. Rush B, Celi L, Stone D. Applying machine learning to continuously monitored physiological data. Journal of Clinical Monitoring and Computing 2019;33(5):887 View
  55. Opal S, Wittebole X. Biomarkers of Infection and Sepsis. Critical Care Clinics 2020;36(1):11 View
  56. Bock C, Gumbsch T, Moor M, Rieck B, Roqueiro D, Borgwardt K. Association mapping in biomedical time series via statistically significant shapelet mining. Bioinformatics 2018;34(13):i438 View
  57. Bergquist T, Yan Y, Schaffter T, Yu T, Pejaver V, Hammarlund N, Prosser J, Guinney J, Mooney S. Piloting a model-to-data approach to enable predictive analytics in health care through patient mortality prediction. Journal of the American Medical Informatics Association 2020;27(9):1393 View
  58. Burdick H, Pino E, Gabel-Comeau D, McCoy A, Gu C, Roberts J, Le S, Slote J, Pellegrini E, Green-Saxena A, Hoffman J, Das R. Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health & Care Informatics 2020;27(1):e100109 View
  59. Bulgarelli L, Deliberato R, Johnson A. Prediction on critically ill patients: The role of “big data”. Journal of Critical Care 2020;60:64 View
  60. Delahanty R, Alvarez J, Flynn L, Sherwin R, Jones S. Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of Emergency Medicine 2019;73(4):334 View
  61. Lin K, Hu Y, Kong G. Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. International Journal of Medical Informatics 2019;125:55 View
  62. Eguia E, Cobb A, Baker M, Joyce C, Gilbert E, Gonzalez R, Afshar M, Churpek M. Risk factors for infection and evaluation of Sepsis-3 in patients with trauma. The American Journal of Surgery 2019;218(5):851 View
  63. Kwan J, Lo L, Ferguson J, Goldberg H, Diaz-Martinez J, Tomlinson G, Grimshaw J, Shojania K. Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ 2020:m3216 View
  64. Davoodi R, Moradi M. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. Journal of Biomedical Informatics 2018;79:48 View
  65. Evangelatos N, Bauer P, Reumann M, Satyamoorthy K, Lehrach H, Brand A. Metabolomics in Sepsis and Its Impact on Public Health. Public Health Genomics 2017;20(5):274 View
  66. Calvert J, Hoffman J, Barton C, Shimabukuro D, Ries M, Chettipally U, Kerem Y, Jay M, Mataraso S, Das R. Cost and mortality impact of an algorithm-driven sepsis prediction system. Journal of Medical Economics 2017;20(6):646 View
  67. Park E, Chang H, Nam H. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120 View
  68. Salluh J, Soares M, Keegan M. Understanding intensive care unit benchmarking. Intensive Care Medicine 2017;43(11):1703 View
  69. Baldominos A, Puello A, Ogul H, Asuroglu T, Colomo-Palacios R. Predicting Infections Using Computational Intelligence – A Systematic Review. IEEE Access 2020;8:31083 View
  70. Bedoya A, Futoma J, Clement M, Corey K, Brajer N, Lin A, Simons M, Gao M, Nichols M, Balu S, Heller K, Sendak M, O’Brien C. Machine learning for early detection of sepsis: an internal and temporal validation study. JAMIA Open 2020;3(2):252 View
  71. Lovejoy C, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. Critical Care 2019;23(1) View
  72. Kindle R, Badawi O, Celi L, Sturland S. Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. Critical Care Clinics 2019;35(3):483 View
  73. Kim J, Chang H, Kim D, Jang D, Park I, Kim K. Machine learning for prediction of septic shock at initial triage in emergency department. Journal of Critical Care 2020;55:163 View
  74. Ruan Y, Bellot A, Moysova Z, Tan G, Lumb A, Davies J, van der Schaar M, Rea R. Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records. Diabetes Care 2020;43(7):1504 View
  75. Pirracchio R, Cohen M, Malenica I, Cohen J, Chambaz A, Cannesson M, Lee C, Resche-Rigon M, Hubbard A. Big data and targeted machine learning in action to assist medical decision in the ICU. Anaesthesia Critical Care & Pain Medicine 2019;38(4):377 View
  76. Scardoni A, Balzarini F, Signorelli C, Cabitza F, Odone A. Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature. Journal of Infection and Public Health 2020;13(8):1061 View
  77. Weldon D, Kowalski R, Schubel L, Schuchardt B, Arnold R, Capan M, Blumenthal J, Franklin E, Catchpole K, Jacob Seagull F, Sanford Schwartz J, Miller K. Signaling Sepsis Scenario Development & Validation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2018;62(1):615 View
  78. Ginestra J, Giannini H, Schweickert W, Meadows L, Lynch M, Pavan K, Chivers C, Draugelis M, Donnelly P, Fuchs B, Umscheid C. Clinician Perception of a Machine Learning–Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock*. Critical Care Medicine 2019;47(11):1477 View
  79. Le S, Hoffman J, Barton C, Fitzgerald J, Allen A, Pellegrini E, Calvert J, Das R. Pediatric Severe Sepsis Prediction Using Machine Learning. Frontiers in Pediatrics 2019;7 View
  80. Shashikumar S, Li Q, Clifford G, Nemati S. Multiscale network representation of physiological time series for early prediction of sepsis. Physiological Measurement 2017;38(12):2235 View
  81. Wang Z, Sun H, Zhao D, Jiang T. Convolution Denoising Regularized Auto Encoder Stacked Method for Coronary Acute Syndrome in Internet of Medical Things Platform. IEEE Access 2020;8:57389 View
  82. Ruminski C, Clark M, Lake D, Kitzmiller R, Keim-Malpass J, Robertson M, Simons T, Moorman J, Calland J. Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit. Journal of Clinical Monitoring and Computing 2019;33(4):703 View
  83. Masino A, Harris M, Forsyth D, Ostapenko S, Srinivasan L, Bonafide C, Balamuth F, Schmatz M, Grundmeier R, Juarez J. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLOS ONE 2019;14(2):e0212665 View
  84. Barbour K, Hesdorffer D, Tian N, Yozawitz E, McGoldrick P, Wolf S, McDonough T, Nelson A, Loddenkemper T, Basma N, Johnson S, Grinspan Z. Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing. Epilepsia 2019;60(6):1209 View
  85. Ocampo-Quintero N, Vidal-Cortés P, del Río Carbajo L, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D. Enhancing sepsis management through machine learning techniques: A review. Medicina Intensiva 2022;46(3):140 View
  86. Keim-Malpass J, Clark M, Lake D, Moorman J. Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. Journal of Clinical Monitoring and Computing 2020;34(4):797 View
  87. Mohamed M, Malewicz N, Zehry H, Hussain D, Barouh J, Cançado A, Silva J, Suwileh S, Carvajal J. Fluid Administration in Emergency Room Limited by Lung Ultrasound in Patients with Sepsis: Protocol for a Prospective Phase II Multicenter Randomized Controlled Trial. JMIR Research Protocols 2020;9(8):e15997 View
  88. Lhommet C, Garot D, Grammatico-Guillon L, Jourdannaud C, Asfar P, Faisy C, Muller G, Barker K, Mercier E, Robert S, Lanotte P, Goudeau A, Blasco H, Guillon A. Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?. BMC Pulmonary Medicine 2020;20(1) View
  89. Rahman M, Tumian A. Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review. Applied Mechanics and Materials 2019;892:274 View
  90. Barchitta M, Maugeri A, Favara G, Riela P, Gallo G, Mura I, Agodi A. A machine learning approach to predict healthcare-associated infections at intensive care unit admission: findings from the SPIN-UTI project. Journal of Hospital Infection 2021;112:77 View
  91. Zhang Y, Zhu S, Yuan Z, Li Q, Ding R, Bao X, Zhen T, Fu Z, Fu H, Xing K, Yuan H, Chen T. Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis. BMC Cancer 2020;20(1) View
  92. Yun H, Park J, Choi D, Shin S, Jang M, Kong H, Kim S. Enhancement in Performance of Septic Shock Prediction Using National Early Warning Score, Initial Triage Information, and Machine Learning Analysis. The Journal of Emergency Medicine 2021;61(1):1 View
  93. Nemeth C, Amos-Binks A, Burris C, Keeney N, Pinevich Y, Pickering B, Rule G, Laufersweiler D, Herasevich V, Sun M. Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock. Military Medicine 2021;186(Supplement_1):273 View
  94. Nesaragi N, Patidar S. Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features. Critical Care Medicine 2020;48(12):e1343 View
  95. Le S, Allen A, Calvert J, Palevsky P, Braden G, Patel S, Pellegrini E, Green-Saxena A, Hoffman J, Das R. Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction. Kidney International Reports 2021;6(5):1289 View
  96. Barchitta M, Maugeri A, Favara G, Riela P, Gallo G, Mura I, Agodi A. Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project. Journal of Clinical Medicine 2021;10(5):992 View
  97. Shashikumar S, Josef C, Sharma A, Nemati S. DeepAISE – An interpretable and recurrent neural survival model for early prediction of sepsis. Artificial Intelligence in Medicine 2021;113:102036 View
  98. Sakib N, Ahamed S, Khan R, Griffin P, Haque M. Unpacking Prevalence and Dichotomy in Quick Sequential Organ Failure Assessment and Systemic Inflammatory Response Syndrome Parameters: Observational Data–Driven Approach Backed by Sepsis Pathophysiology. JMIR Medical Informatics 2020;8(12):e18352 View
  99. He Z, Du L, Zhang P, Zhao R, Chen X, Fang Z. Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records. Critical Care Medicine 2020;48(12):e1337 View
  100. Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns G, Ghayour-Mobarhan M. Potential value and impact of data mining and machine learning in clinical diagnostics. Critical Reviews in Clinical Laboratory Sciences 2021;58(4):275 View
  101. Giacobbe D, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Frontiers in Medicine 2021;8 View
  102. Alexander J, Romito B, Çobanoğlu M. The present and future role of artificial intelligence and machine learning in anesthesiology. International Anesthesiology Clinics 2020;58(4):7 View
  103. Radhachandran A, Garikipati A, Zelin N, Pellegrini E, Ghandian S, Calvert J, Hoffman J, Mao Q, Das R. Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Mining 2021;14(1) View
  104. Wardi G, Carlile M, Holder A, Shashikumar S, Hayden S, Nemati S. Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm. Annals of Emergency Medicine 2021;77(4):395 View
  105. Liu X, Liu T, Zhang Z, Kuo P, Xu H, Yang Z, Lan K, Li P, Ouyang Z, Ng Y, Yan W, Li D. TOP-Net Prediction Model Using Bidirectional Long Short-term Memory and Medical-Grade Wearable Multisensor System for Tachycardia Onset: Algorithm Development Study. JMIR Medical Informatics 2021;9(4):e18803 View
  106. Supriya M, Deepa A. Machine learning approach on healthcare big data: a review. Big Data and Information Analytics 2020;5(1):58 View
  107. Al-Shwaheen T, Moghbel M, Hau Y, Ooi C. Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature. Artificial Intelligence Review 2022;55(2):1055 View
  108. Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, Slight S. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. International Journal of Medical Informatics 2021;150:104457 View
  109. Rafiei A, Rezaee A, Hajati F, Gheisari S, Golzan M. SSP: Early prediction of sepsis using fully connected LSTM-CNN model. Computers in Biology and Medicine 2021;128:104110 View
  110. Burdick H, Pino E, Gabel-Comeau D, Gu C, Roberts J, Le S, Slote J, Saber N, Pellegrini E, Green-Saxena A, Hoffman J, Das R. Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Medical Informatics and Decision Making 2020;20(1) View
  111. Jalilian L, Cannesson M. Precision medicine in anesthesiology. International Anesthesiology Clinics 2020;58(4):17 View
  112. Tabaie A, Orenstein E, Nemati S, Basu R, Kandaswamy S, Clifford G, Kamaleswaran R. Predicting presumed serious infection among hospitalized children on central venous lines with machine learning. Computers in Biology and Medicine 2021;132:104289 View
  113. Goh K, Wang L, Yeow A, Poh H, Li K, Yeow J, Tan G. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature Communications 2021;12(1) View
  114. Schvetz M, Fuchs L, Novack V, Moskovitch R. Outcomes prediction in longitudinal data: Study designs evaluation, use case in ICU acquired sepsis. Journal of Biomedical Informatics 2021;117:103734 View
  115. Steinmeyer C, Wiese L. Sampling methods and feature selection for mortality prediction with neural networks. Journal of Biomedical Informatics 2020;111:103580 View
  116. Wernly B, Mamandipoor B, Baldia P, Jung C, Osmani V. Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation. International Journal of Medical Informatics 2021;145:104312 View
  117. Muralitharan S, Nelson W, Di S, McGillion M, Devereaux P, Barr N, Petch J. Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review. Journal of Medical Internet Research 2021;23(2):e25187 View
  118. Liu Z, Khojandi A, Mohammed A, Li X, Chinthala L, Davis R, Kamaleswaran R. HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study. Computers in Biology and Medicine 2021;131:104255 View
  119. Annapragada A, Donaruma-Kwoh M, Annapragada A, Starosolski Z, Le K. A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records. PLOS ONE 2021;16(2):e0247404 View
  120. Tang S, Davarmanesh P, Song Y, Koutra D, Sjoding M, Wiens J. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. Journal of the American Medical Informatics Association 2020;27(12):1921 View
  121. Aşuroğlu T, Oğul H. A deep learning approach for sepsis monitoring via severity score estimation. Computer Methods and Programs in Biomedicine 2021;198:105816 View
  122. von Wedel P, Hagist C. Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review. Journal of Medical Internet Research 2020;22(11):e23315 View
  123. Alvi R, Rahman M, Khan A, Rahman R. Deep learning approach on tabular data to predict early-onset neonatal sepsis. Journal of Information and Telecommunication 2021;5(2):226 View
  124. Raspa M, Paquin R, Brown D, Andrews S, Edwards A, Moultrie R, Wagner L, Frisch M, Turner-Brown L, Wheeler A. Preferences for Accessing Electronic Health Records for Research Purposes: Views of Parents Who Have a Child With a Known or Suspected Genetic Condition. Value in Health 2020;23(12):1639 View
  125. Syed M, Syed S, Sexton K, Syeda H, Garza M, Zozus M, Syed F, Begum S, Syed A, Sanford J, Prior F. Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review. Informatics 2021;8(1):16 View
  126. Sun H, Depraetere K, Meesseman L, De Roo J, Vanbiervliet M, De Baerdemaeker J, Muys H, von Dossow V, Hulde N, Szymanowsky R. A scalable approach for developing clinical risk prediction applications in different hospitals. Journal of Biomedical Informatics 2021;118:103783 View
  127. Kausch S, Moorman J, Lake D, Keim-Malpass J. Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review. Intensive and Critical Care Nursing 2021;65:103035 View
  128. Taneja I, Damhorst G, Lopez‐Espina C, Zhao S, Zhu R, Khan S, White K, Kumar J, Vincent A, Yeh L, Majdizadeh S, Weir W, Isbell S, Skinner J, Devanand M, Azharuddin S, Meenakshisundaram R, Upadhyay R, Syed A, Bauman T, Devito J, Heinzmann C, Podolej G, Shen L, Timilsina S, Quinlan L, Manafirasi S, Valera E, Reddy B, Bashir R. Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis. Clinical and Translational Science 2021;14(4):1578 View
  129. Nesaragi N, Patidar S, Aggarwal V. Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis. Computers in Biology and Medicine 2021;134:104430 View
  130. Rosnati M, Fortuin V, Olier I. MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis. PLOS ONE 2021;16(5):e0251248 View
  131. Li K, Shi Q, Liu S, Xie Y, Liu J. Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree. Medicine 2021;100(19):e25813 View
  132. Park H, Song M, Lee E, Seo B, Choi C. An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation. JMIR Medical Informatics 2021;9(5):e24803 View
  133. Liu X, Anstey J, Li R, Sarabu C, Sono R, Butte A. Rethinking PICO in the Machine Learning Era: ML-PICO. Applied Clinical Informatics 2021;12(02):407 View
  134. Moor M, Rieck B, Horn M, Jutzeler C, Borgwardt K. Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine 2021;8 View
  135. Lee J, Molani S, Fang C, Jade K, O'Mahony D, Kornilov S, Mico L, Hadlock J. Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study. JMIR Medical Informatics 2021;9(7):e29986 View
  136. Yao S, Yang J, Xu D, Dark P. Probabilistic modeling approach for interpretable inference and prediction with data for sepsis diagnosis. Expert Systems with Applications 2021;183:115333 View
  137. Xiang L, Wang H, Fan S, Zhang W, Lu H, Dong B, Liu S, Chen Y, Wang Y, Zhao L, Fu L. Machine Learning for Early Warning of Septic Shock in Children With Hematological Malignancies Accompanied by Fever or Neutropenia: A Single Center Retrospective Study. Frontiers in Oncology 2021;11 View
  138. Hauschild A, Eick L, Wienbeck J, Heider D. Fostering reproducibility, reusability, and technology transfer in health informatics. iScience 2021;24(7):102803 View
  139. Wingert T, Lee C, Cannesson M. Machine Learning, Deep Learning, and Closed Loop Devices—Anesthesia Delivery. Anesthesiology Clinics 2021;39(3):565 View
  140. Persson I, Östling A, Arlbrandt M, Söderberg J, Becedas D. A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study. JMIR Formative Research 2021;5(9):e28000 View
  141. Kopanitsa G, Metsker O, Paskoshev D, Greschischeva S. Identification of Risk Factors and Prediction of Sepsis in Pregnancy Using Machine Learning Methods. Procedia Computer Science 2021;193:393 View
  142. Dellinger R, Levy M, Schorr C, Townsend S. 50 Years of Sepsis Investigation/Enlightenment Among Adults—The Long and Winding Road. Critical Care Medicine 2021;49(10):1606 View
  143. Kim J, Ahn W, Park S, Lee S, Kim L. Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms. International Journal of Environmental Research and Public Health 2022;19(4):2349 View
  144. Forni L. Blood Purification Studies in the ICU: What Endpoints Should We Use?. Blood Purification 2022;51(12):990 View
  145. Singh Y, Singh P, Khan S, Singh R, Gupta S. A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients. Journal of Healthcare Engineering 2022;2022:1 View
  146. Lam C, Thapa R, Maharjan J, Rahmani K, Tso C, Singh N, Casie Chetty S, Mao Q. Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study. JMIR Medical Informatics 2022;10(6):e36202 View
  147. Joshi M, Mecklai K, Rozenblum R, Samal L. Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study. JAMIA Open 2022;5(2) View
  148. Baird A, Amos-Binks A, Tatum N, White S, Hackett M, Serio-Melvin M. Detecting Patient Health Trajectories Using a Full-Body Burn Physiology Model. BioMedInformatics 2021;1(3):127 View
  149. Ameen S, Wong M, Yee K, Turner P. AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. Applied Sciences 2022;12(7):3341 View
  150. Wu X, Li R, He Z, Yu T, Cheng C. A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis. npj Digital Medicine 2023;6(1) View
  151. . The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden. Journal of Health Economics and Outcomes Research 2022;9(1):101 View
  152. Silvestri J, Kmiec T, Bishop N, Regli S, Weissman G. Desired Characteristics of a Clinical Decision Support System for Early Sepsis Recognition: Interview Study Among Hospital-Based Clinicians. JMIR Human Factors 2022;9(4):e36976 View
  153. Williams E, Kienast M, Medawar E, Reinelt J, Merola A, Klopfenstein S, Flint A, Heeren P, Poncette A, Balzer F, Beimes J, von Bünau P, Chromik J, Arnrich B, Scherf N, Niehaus S. A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study. JMIR Medical Informatics 2023;11:e43847 View
  154. Adams R, Henry K, Sridharan A, Soleimani H, Zhan A, Rawat N, Johnson L, Hager D, Cosgrove S, Markowski A, Klein E, Chen E, Saheed M, Henley M, Miranda S, Houston K, Linton R, Ahluwalia A, Wu A, Saria S. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine 2022;28(7):1455 View
  155. Gosset C, Foguenne J, Simul M, Tomsin O, Ammar H, Layios N, Massion P, Damas P, Gothot A. Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections. Scientific Reports 2021;11(1) View
  156. Choi A, Chung K, Chung S, Lee K, Hyun H, Kim J. Advantage of Vital Sign Monitoring Using a Wireless Wearable Device for Predicting Septic Shock in Febrile Patients in the Emergency Department: A Machine Learning-Based Analysis. Sensors 2022;22(18):7054 View
  157. Chicco D, Jurman G. An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis. IEEE Access 2021;9:24485 View
  158. Zargoush M, Sameh A, Javadi M, Shabani S, Ghazalbash S, Perri D. The impact of recency and adequacy of historical information on sepsis predictions using machine learning. Scientific Reports 2021;11(1) View
  159. Wu K, Cheng F, Tai H, Wang J, Huang Y, Su C, Chang Y. Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach. PeerJ 2021;9:e11988 View
  160. El-Rashidy N, Abuhmed T, Alarabi L, El-Bakry H, Abdelrazek S, Ali F, El-Sappagh S. Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning. Neural Computing and Applications 2022;34(5):3603 View
  161. Kamaleswaran R, Sataphaty S, Mas V, Eason J, Maluf D. Artificial Intelligence May Predict Early Sepsis After Liver Transplantation. Frontiers in Physiology 2021;12 View
  162. Alba-Patiño A, Vaquer A, Barón E, Russell S, Borges M, de la Rica R. Micro- and nanosensors for detecting blood pathogens and biomarkers at different points of sepsis care. Microchimica Acta 2022;189(2) View
  163. Allen A, Iqbal Z, Green-Saxena A, Hurtado M, Hoffman J, Mao Q, Das R. Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research & Care 2022;10(1):e002560 View
  164. Chen Q, Li R, Lin C, Lai C, Huang Y, Lu W, Li L. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis. Applied Clinical Informatics 2023;14(01):65 View
  165. Wang D, Li J, Sun Y, Ding X, Zhang X, Liu S, Han B, Wang H, Duan X, Sun T. A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Frontiers in Public Health 2021;9 View
  166. Kiser A, Eilbeck K, Ferraro J, Skarda D, Samore M, Bucher B. Standard Vocabularies to Improve Machine Learning Model Transferability With Electronic Health Record Data: Retrospective Cohort Study Using Health Care–Associated Infection. JMIR Medical Informatics 2022;10(8):e39057 View
  167. Albert K, Delano M. Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records. Patterns 2022;3(8):100534 View
  168. Duan Y, Huo J, Chen M, Hou F, Yan G, Li S, Wang H. Early prediction of sepsis using double fusion of deep features and handcrafted features. Applied Intelligence 2023;53(14):17903 View
  169. Tang M, Mu F, Cui C, Zhao J, Lin R, Sun K, Guan Y, Wang J. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Frontiers in Medicine 2023;9 View
  170. Saab A, Abi Khalil C, Jammal M, Saikali M, Lamy J. Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations. Journal of Patient Safety 2022;18(6):578 View
  171. Sarwar T, Seifollahi S, Chan J, Zhang X, Aksakalli V, Hudson I, Verspoor K, Cavedon L. The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges. ACM Computing Surveys 2023;55(2):1 View
  172. Oei S, van Sloun R, van der Ven M, Korsten H, Mischi M. Towards early sepsis detection from measurements at the general ward through deep learning. Intelligence-Based Medicine 2021;5:100042 View
  173. Pankhurst T, Lucas L, Ryan S, Ragdale C, Gyves H, Denner L, Young I, Rathbone L, Shah A, McKee D, Coleman J, Evison F, Atia J, Rosser D, Garrick M, Baker R, Gallier S, Ball S. Benefits of electronic charts in intensive care and during a world health pandemic: advantages of the technology age. BMJ Open Quality 2023;12(1):e001704 View
  174. Mercurio L, Pou S, Duffy S, Eickhoff C. Risk Factors for Pediatric Sepsis in the Emergency Department. Pediatric Emergency Care 2023;39(2):e48 View
  175. Selcuk M, Koc O, Kestel A. The prediction power of machine learning on estimating the sepsis mortality in the intensive care unit. Informatics in Medicine Unlocked 2022;28:100861 View
  176. Vitik A, Shen N, Sukhanova N, Pylenko L. Predictors of the Generalization of Infectious Process. Annals of Critical Care 2017;(3):63 View
  177. Хассанин Х. Распознавание сепсиса в отделении интенсивной терапии новорожденных с использованием искусственной нейронной сети. Естественные и Технические Науки 2021;(№11):117 View
  178. Hong N, Liu C, Gao J, Han L, Chang F, Gong M, Su L. State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review. JMIR Medical Informatics 2022;10(3):e28781 View
  179. Kijpaisalratana N, Sanglertsinlapachai D, Techaratsami S, Musikatavorn K, Saoraya J. Machine learning algorithms for early sepsis detection in the emergency department: A retrospective study. International Journal of Medical Informatics 2022;160:104689 View
  180. Eini-Porat B, Amir O, Eytan D, Shalit U. Tell me something interesting: Clinical utility of machine learning prediction models in the ICU. Journal of Biomedical Informatics 2022;132:104107 View
  181. Rakhit S, Wang L, Lindsell C, Hosay M, Stewart J, Owen G, Frutos-Vivar F, Pen~uelas O, Esteban A, Anzueto A, Raymondos K, Rios F, Thille A, Gonza´lez M, Du B, Maggiore S, Matamis D, Abroug F, Amin P, Zeggwagh A, Ely E, Vasilevskis E, Patel M. Multicenter International Cohort Validation of a Modified Sequential Organ Failure Assessment Score Using the Richmond Agitation-sedation Scale. Annals of Surgery 2022;276(2):e114 View
  182. Ocampo-Quintero N, Vidal-Cortés P, del Río Carbajo L, Fdez-Riverola F, Reboiro-Jato M, Glez-Peña D. Enhancing sepsis management through machine learning techniques: A review. Medicina Intensiva (English Edition) 2022;46(3):140 View
  183. El-Kareh R, Sittig D. Enhancing Diagnosis Through Technology. Critical Care Clinics 2022;38(1):129 View
  184. Lombardi S, Partanen P, Francia P, Calamai I, Deodati R, Luchini M, Spina R, Bocchi L. Classifying sepsis from photoplethysmography. Health Information Science and Systems 2022;10(1) View
  185. Chen X, Zhang R, Tang X, Qian J. Prediction of Pediatric Sepsis Using a Deep Encoding Network with Cross Features. Journal of Shanghai Jiaotong University (Science) 2024;29(1):131 View
  186. Camacho-Cogollo J, Bonet I, Gil B, Iadanza E. Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets. Electronics 2022;11(9):1507 View
  187. Liu Z, Khojandi A, Li X, Mohammed A, Davis R, Kamaleswaran R. A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction. INFORMS Journal on Computing 2022;34(4):2039 View
  188. Lipatov K, Daniels C, Park J, Elmer J, Hanson A, Madsen B, Clements C, Gajic O, Pickering B, Herasevich V. Implementation and evaluation of sepsis surveillance and decision support in medical ICU and emergency department. The American Journal of Emergency Medicine 2022;51:378 View
  189. Zoabi Y, Kehat O, Lahav D, Weiss-Meilik A, Adler A, Shomron N. Predicting bloodstream infection outcome using machine learning. Scientific Reports 2021;11(1) View
  190. Apalak M, Kiasaleh K. Improving Sepsis Prediction Performance Using Conditional Recurrent Adversarial Networks. IEEE Access 2022;10:134466 View
  191. Martin B, DeWitt P, Scott H, Parker S, Bennett T. Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission. Hospital Pediatrics 2022;12(6):590 View
  192. Wu C, Wu M, Chen L, Lo Y, Huang C, Yu H, Pardeshi M, Lo W, Sheu R. AEP-DLA: Adverse Event Prediction in Hospitalized Adult Patients Using Deep Learning Algorithms. IEEE Access 2021;9:55673 View
  193. Zhao X, Shen W, Wang G, Schwenker F. Early Prediction of Sepsis Based on Machine Learning Algorithm. Computational Intelligence and Neuroscience 2021;2021(1) View
  194. Li M, Du S. Current status and trends in researches based on public intensive care databases: A scientometric investigation. Frontiers in Public Health 2022;10 View
  195. Guo F, Zhu X, Wu Z, Zhu L, Wu J, Zhang F. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter. Journal of Translational Medicine 2022;20(1) View
  196. Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Medical Informatics and Decision Making 2022;22(1) View
  197. Tabaie A, Orenstein E, Nemati S, Basu R, Clifford G, Kamaleswaran R. Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines. Frontiers in Pediatrics 2021;9 View
  198. Yu S, Gupta A, Betthauser K, Lyons P, Lai A, Kollef M, Payne P, Michelson A. Sepsis Prediction for the General Ward Setting. Frontiers in Digital Health 2022;4 View
  199. ÇAYIRTEPE Z, ŞENEL A. Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records. Journal of Basic and Clinical Health Sciences 2022;6(3):958 View
  200. Shashikumar S, Wardi G, Malhotra A, Nemati S. Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”. npj Digital Medicine 2021;4(1) View
  201. Wu Y, Huang S, Chang X. Understanding the complexity of sepsis mortality prediction via rule discovery and analysis: a pilot study. BMC Medical Informatics and Decision Making 2021;21(1) View
  202. Lindner H, Schamoni S, Kirschning T, Worm C, Hahn B, Centner F, Schoettler J, Hagmann M, Krebs J, Mangold D, Nitsch S, Riezler S, Thiel M, Schneider-Lindner V. Ground truth labels challenge the validity of sepsis consensus definitions in critical illness. Journal of Translational Medicine 2022;20(1) View
  203. Kendale S. The role of artificial intelligence in preoperative medicine. International Anesthesiology Clinics 2022;60(1):69 View
  204. Anyalebechi J, Coopersmith C. Early Clinical Management of Sepsis: Past, Present, and Future. Journal of Translational Critical Care Medicine 2022;4(1):14 View
  205. Maharjan J, Thapa R, Calvert J, Attwood M, Shokouhi S, Casie Chetty S, Iqbal Z, Singh N, Arnold R, Hoffman J, Mataraso S, Garikipati A, Lam C, Mao Q. A New Standard for Sepsis Prediction Algorithms: Using Time-Dependent Analysis for Earlier Clinically Relevant Alerts. SSRN Electronic Journal 2022 View
  206. Rafiei A, Hajati F, Rezaee A, Panahi A, Uddin S. Early Prediction of Sepsis Using Heart Rate Signals in Wearable Devices with Genetic Optimized Lstm Algorithm. SSRN Electronic Journal 2022 View
  207. Patton M, Liu V. Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data. Critical Care Clinics 2023;39(4):647 View
  208. van der Vegt A, Scott I, Dermawan K, Schnetler R, Kalke V, Lane P. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. Journal of the American Medical Informatics Association 2023;30(7):1349 View
  209. 何 亚. Sepsis Prediction Method Based on Neural Network Combined with Multiple Life Characteristics. Modeling and Simulation 2023;12(03):2306 View
  210. Li C, Zhou R, Chen G, Hao X, Zhu T. Knowledge mapping and research hotspots of artificial intelligence on ICU and Anesthesia: from a global bibliometric perspective. Anesthesiology and Perioperative Science 2023;1(4) View
  211. Islam K, Prithula J, Kumar J, Tan T, Reaz M, Sumon M, Chowdhury M. Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review. Journal of Clinical Medicine 2023;12(17):5658 View
  212. Jadhav P, Sears T, Floan G, Joskowitz K, Nienow S, Cruz S, David M, de Cos V, Choi P, Ignacio R. Application of a Machine Learning Algorithm in Prediction of Abusive Head Trauma in Children. Journal of Pediatric Surgery 2024;59(1):80 View
  213. Lejarza F, Calvert J, Attwood M, Evans D, Mao Q. Optimal discharge of patients from intensive care via a data-driven policy learning framework. Operations Research for Health Care 2023;38:100400 View
  214. Alanazi A, Aldakhil L, Aldhoayan M, Aldosari B. Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients. Medicina 2023;59(7):1276 View
  215. Cimino J, Braun C. Clinical Research in Prehospital Care: Current and Future Challenges. Clinics and Practice 2023;13(5):1266 View
  216. Cheng T, Ho S, Chien T, Chow J, Chou W. A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis. Medicine 2023;102(42):e35156 View
  217. Bennett N, Plečko D, Ukor I, Meinshausen N, Bühlmann P. ricu: R’s interface to intensive care data. GigaScience 2022;12 View
  218. Valik J, Ward L, Tanushi H, Johansson A, Färnert A, Mogensen M, Pickering B, Herasevich V, Dalianis H, Henriksson A, Nauclér P. Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data. Scientific Reports 2023;13(1) View
  219. Lashen H, St John T, Almallah Y, Sasidhar M, Shamout F. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023;2:e45257 View
  220. Yoon B, Seol C, Min I, Park M, Park J, Chung K. Biomarker-Based Assessment Model for Detecting Sepsis: A Retrospective Cohort Study. Journal of Personalized Medicine 2023;13(8):1195 View
  221. King A, Tang L, Davis B, Preum S, Bukowski L, Zimmerman J, Kahn J. Machine learning-based prediction of low-value care for hospitalized patients. Intelligence-Based Medicine 2023;8:100115 View
  222. Cheng T, Yu-Chieh Ho S, Chien T, Chou W. Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis. Medicine 2023;102(38):e35082 View
  223. Kim T, Tae Y, Yeo H, Jang J, Cho K, Yoo D, Lee Y, Ahn S, Kim Y, Lee N, Cho W. Development and Validation of Deep-Learning-Based Sepsis and Septic Shock Early Prediction System (DeepSEPS) Using Real-World ICU Data. Journal of Clinical Medicine 2023;12(22):7156 View
  224. van der Vegt A, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane P, Mehta N, Kalke V, Decoyna J, Es’haghi N, Liu C, Scott I. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. Journal of the American Medical Informatics Association 2024;31(2):509 View
  225. Litvinova O, Hammerle F, Stoyanov J, Ksepka N, Matin M, Ławiński M, Atanasov A, Willschke H. Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine. Healthcare 2023;11(22):3003 View
  226. Solís-García J, Vega-Márquez B, Nepomuceno J, Riquelme-Santos J, Nepomuceno-Chamorro I. Comparing artificial intelligence strategies for early sepsis detection in the ICU: an experimental study. Applied Intelligence 2023;53(24):30691 View
  227. Cohen S, Foster J, Foster P, Lou H, Lyons T, Morley S, Morrill J, Ni H, Palmer E, Wang B, Wu Y, Yang L, Yang W. Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods. Scientific Reports 2024;14(1) View
  228. Rashid A, Al-Obeida F, Hafez W, Benakatti G, Malik R, Koutentis C, Sharief J, Brierley J, Quraishi N, Malik Z, Anwary A, Alkhzaimi H, Zaki S, Khilnani P, Kadwa R, Phatak R, Schumacher M, Shaikh G, Al-Dubai A, Hussain A. Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes. Shock 2023 View
  229. Li Q, Ma H, Song D, Bai Y, Zhao L, Xie K. Early prediction of sepsis using chatGPT-generated summaries and structured data. Multimedia Tools and Applications 2024 View
  230. Soliman M, Marshall C, Kimball J, Choudhary T, Clermont G, Pinsky M, Buchman T, Coopersmith C, Inan O, Kamaleswaran R. Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock. Biomedical Signal Processing and Control 2024;92:105974 View
  231. Kijpaisalratana N, Saoraya J, Nhuboonkaew P, Vongkulbhisan K, Musikatavorn K. Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial. Internal and Emergency Medicine 2024;19(5):1415 View
  232. Gao T, Nong Z, Luo Y, Mo M, Chen Z, Yang Z, Pan L. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury. Renal Failure 2024;46(1) View
  233. Choi D, Choi S, Kim K, Choi Y, Kim Y. Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department. The American Journal of Emergency Medicine 2024;80:67 View
  234. Mollura M, Chicco D, Paglialonga A, Barbieri R, Liu N. Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records. PLOS Digital Health 2024;3(3):e0000459 View
  235. Shenderovitz G, Sheetrit E, Nissim N. Patterns of time-interval based patterns for improved multivariate time series data classification. Engineering Applications of Artificial Intelligence 2024;133:108171 View
  236. Lin Y, Chang T, Liu C, Lai C, Chen C, Chou W. The intervention of artificial intelligence to improve the weaning outcomes of patients with mechanical ventilation: Practical applications in the medical intensive care unit and the COVID-19 intensive care unit: A retrospective study. Medicine 2024;103(12):e37500 View
  237. Burton R, Raffray L, Moet L, Cuff S, White D, Baker S, Moser B, O’Donnell V, Ghazal P, Morgan M, Artemiou A, Eberl M. Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. Clinical and Experimental Immunology 2024;216(3):293 View
  238. Wu M, Islam M, Poly T, Lin M. Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis. Interactive Journal of Medical Research 2024;13:e54490 View
  239. Chen M, Huo J, Duan Y. An interpretable model for sepsis prediction using multi-objective rule extraction. Journal of Intelligent Information Systems 2024;62(5):1403 View
  240. Mutlu B, Yeşilyurt M, Shahbazi N, Güzel M, Sezer E. Early prediction of Sepsis: A comparative assessment on patients’ covariates. Biomedical Signal Processing and Control 2024;95:106400 View
  241. Wang L, Tian W, Zhang W, Wen D, Yang S, Wang J, Han X, Wang J, Ding W, Wang L, Yu Y, Wu W. A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing. Clinica Chimica Acta 2024;559:119716 View
  242. Mao Q, Jay M, Hoffman J, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open 2018;8(1):e017833 View
  243. Desautels T, Das R, Calvert J, Trivedi M, Summers C, Wales D, Ercole A. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach. BMJ Open 2017;7(9):e017199 View
  244. Shimabukuro D, Barton C, Feldman M, Mataraso S, Das R. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respiratory Research 2017;4(1):e000234 View
  245. Das P, Wiese L, Mast M, Böhnke J, Wulff A, Marschollek M, Bode L, Rathert H, Jack T, Schamer S, Beerbaum P, Rübsamen N, Karch A, Groszweski-Anders C, Haller A, Frank T. An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis. International Journal of Data Science and Analytics 2024 View
  246. Pérez-Tome J, Parrón-Carreño T, Castaño-Fernández A, Nievas-Soriano B, Castro-Luna G. Sepsis mortality prediction with Machine Learning Tecniques. Medicina Intensiva (English Edition) 2024;48(10):584 View
  247. Scott I, De Guzman K, Falconer N, Canaris S, Bonilla O, McPhail S, Marxen S, Van Garderen A, Abdel-Hafez A, Barras M. Evaluating automated machine learning platforms for use in healthcare. JAMIA Open 2024;7(2) View
  248. Kuan L, Chin L, De L, Cheng C, Tuao Z, Zixian Y, Roy D, Roy D. Predictive Analysis of Patient Risk of Death in ICU: A Bibliometric Analysis. SHS Web of Conferences 2024;194:01005 View
  249. Kaur I, Ahmad T, Doja M. A Systematic Review of Medical Expert Systems for Cardiac Arrest Prediction. Current Bioinformatics 2024;19(6):551 View
  250. Schmulevich D, Hynes A, Murali S, Benjamin A, Cannon J. Optimizing damage control resuscitation through early patient identification and real‐time performance improvement. Transfusion 2024;64(8):1551 View
  251. Molaei S, Bousejin N, Ghosheh G, Thakur A, Chauhan V, Zhu T, Clifton D. CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks. Journal of Healthcare Informatics Research 2024;8(3):555 View
  252. Alge O, Pickard J, Zhang W, Cheng S, Derksen H, Omenn G, Gryak J, VanEpps J, Najarian K. Continuous sepsis trajectory prediction using tensor-reduced physiological signals. Scientific Reports 2024;14(1) View
  253. Gupta A, Chauhan R, G S, Shreekumar A, Wang F. Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms. PLOS Digital Health 2024;3(8):e0000569 View
  254. Matos J, Gallifant J, Chowdhury A, Economou-Zavlanos N, Charpignon M, Gichoya J, Celi L, Nazer L, King H, Wong A. A Clinician’s Guide to Understanding Bias in Critical Clinical Prediction Models. Critical Care Clinics 2024;40(4):827 View
  255. Zeydan E, Arslan S, Liyanage M. Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud. IEEE Access 2024;12:115750 View
  256. Özmen E, Emir B. The Role of Machine Learning Algorithms in Sepsis Diagnosis: A Retrospective Overview using Bibliometric Analysis. OSMANGAZİ JOURNAL OF MEDICINE 2024;46(6) View
  257. Pérez-Tome J, Parrón-Carreño T, Castaño-Fernández A, Nievas-Soriano B, Castro-Luna G. Sepsis mortality prediction with Machine Learning Tecniques. Medicina Intensiva 2024;48(10):584 View
  258. Hou Y, Wu M, Chen Y, Liu T, Cheng R, Hsu P, Chao A, Huang C, Cheng F, Lai P, Wu I, Yiang G. EFFICACY OF A SEPSIS CLINICAL DECISION SUPPORT SYSTEM IN IDENTIFYING PATIENTS WITH SEPSIS IN THE EMERGENCY DEPARTMENT. Shock 2024;62(4):480 View
  259. Yadgarov M, Landoni G, Berikashvili L, Polyakov P, Kadantseva K, Smirnova A, Kuznetsov I, Shemetova M, Yakovlev A, Likhvantsev V. Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis. Frontiers in Medicine 2024;11 View
  260. Giglio A, Aranda M, Ferre A, Borges M. Adult Code Sepsis: A Narrative Review of its Implementation and Impact. Journal of Intensive Care Medicine 2024 View
  261. Mai H, Lu Y, Fu Y, Luo T, Li X, Zhang Y, Liu Z, Zhang Y, Zhou S, Chen C. Identification of Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Elderly Patients: A Machine Learning-Based Predictive Model (Preprint). Journal of Medical Internet Research 2024 View
  262. Lin N, Paul R, Guerra S, Liu Y, Doulgeris J, Shi M, Lin M, Engeberg E, Hashemi J, Vrionis F. The Frontiers of Smart Healthcare Systems. Healthcare 2024;12(23):2330 View
  263. B. H, D.K. M, T.M. R, W. B, R. W, V. V, J. D, J. R, F.J. D, P. G, A.H. H. Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review. Artificial Intelligence in Medicine 2025;160:103008 View
  264. Choi A, Lee K, Hyun H, Kim K, Ahn B, Lee K, Hahn S, Choi S, Kim J. A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system. Scientific Reports 2024;14(1) View
  265. Lee J, Han I, Kwon O, Seo H, Jung J, Yoon S, Han A, Lee J, Lee S, Seo H, Kwon W, Eom B, Lee I, Park J, Lee H, Hwang H, Lee S, Shin E, Lee W. Development of the Korean Quality Improvement Platform in Surgery (K-QIPS) program: a nationwide project to improve surgical quality and patient safety. Annals of Surgical Treatment and Research 2024;107(6):305 View
  266. 崔 翔. Research Progress of Deep Learning in Diagnosis of Sepsis. Journal of Clinical Personalized Medicine 2024;03(04):1676 View
  267. Mușat F, Păduraru D, Bolocan A, Palcău C, Copăceanu A, Ion D, Jinga V, Andronic O. Machine Learning Models in Sepsis Outcome Prediction for ICU Patients: Integrating Routine Laboratory Tests—A Systematic Review. Biomedicines 2024;12(12):2892 View

Books/Policy Documents

  1. Bulgarelli L, Núñez-Reiz A, Deliberato R. Leveraging Data Science for Global Health. View
  2. Sharma N, Gautam S, Henry A, Kumar A. Machine Learning and Big Data. View
  3. Xie J, Coopersmith C. Handbook of Sepsis. View
  4. Berikol G, Berikol G. Artificial Intelligence in Precision Health. View
  5. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. View
  6. Thomas M, Abraham D, Liu D. Interdisciplinary Approaches to Digital Transformation and Innovation. View
  7. Chaudhary P, Gupta D, Singh S. Advances in Communication and Computational Technology. View
  8. Bock C, Moor M, Jutzeler C, Borgwardt K. Artificial Neural Networks. View
  9. Pérez-Fernández J, Raimondi N, Murillo Cabezas F. Critical Care Administration. View
  10. Sagi T, Shmueli N, Friedman B, Bergman R. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. View
  11. Sailaja N, Yelamarthi M, Chandana Y, Karadi P, Yedla S. Machine Learning Technologies and Applications. View
  12. Silva J, Villareal-González R, Varela N, Maco J, Villón M, Marín–González F, Lezama O. Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. View
  13. Nayyar A, Gadhavi L, Zaman N. Machine Learning and the Internet of Medical Things in Healthcare. View
  14. Nesaragi N, Patidar S. Infections and Sepsis Development. View
  15. Sa M, Crespo R. The Sepsis Codex. View
  16. Luo K, Li J, Zhao Y. LISS 2021. View
  17. Rayan Z, Alfonse M, Salem A. Digital Transformation Technology. View
  18. Schinkel M, Paranjape K, Nanayakkara P, Wiersinga W. The Sepsis Codex. View
  19. Hermelin T, Singer P, Rappoport N. Artificial Intelligence in Medicine. View
  20. Tsang W, Benoit D. Living Beyond Data. View
  21. Sharma A, Dasgupta D, Bose S, Misra U, Pahari I, Karmakar R, Pal S. Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing. View
  22. Rayan Z, Alfonse M, Salem A. Digital Transformation Technology. View
  23. Ibáñez-Redin G, Duarte O, Cagnani G, Oliveira O. Machine Learning for Advanced Functional Materials. View
  24. Lydia E, Althubiti S, Anupama C, Kumar K. Intelligent Data Engineering and Analytics. View
  25. Shanthi N, Aadhishri A, Suganthe R, Gao X. Computational Sciences and Sustainable Technologies. View
  26. Winter A, Kirsten T, Hartwig M. Biomedical Engineering Systems and Technologies. View
  27. Jain D, Gupta A, Pandey A, Vats P. Reshaping Intelligent Business and Industry. View