Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 30.09.16 in Vol 4, No 3 (2016): Jul-Sept

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

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

According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.5909):

(note that this is only a small subset of citations)

  1. Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology. Anesthesiology 2020;132(2):379
    CrossRef
  2. Ruan Y, Bellot A, Moysova Z, Tan GD, 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
    CrossRef
  3. Sendak MP, 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 MC, 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
    CrossRef
  4. 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
    CrossRef
  5. Abebe Tadesse G, Javed H, Thanh NLN, Ha Thai HD, Le Van T, Thwaites L, Clifton D, Zhu T. Multi-modal Diagnosis of Infectious Diseases in the Developing World. IEEE Journal of Biomedical and Health Informatics 2020;:1
    CrossRef
  6. Bates DW, 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
    CrossRef
  7. Fleuren LM, Klausch TLT, Zwager CL, Schoonmade LJ, Guo T, Roggeveen LF, Swart EL, Girbes ARJ, Thoral P, Ercole A, Hoogendoorn M, Elbers PWG. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine 2020;46(3):383
    CrossRef
  8. Baldominos A, Puello A, Ogul H, Asuroglu T, Colomo-Palacios R. Predicting Infections Using Computational Intelligence – A Systematic Review. IEEE Access 2020;8:31083
    CrossRef
  9. Bedoya AD, Futoma J, Clement ME, Corey K, Brajer N, Lin A, Simons MG, 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
    CrossRef
  10. 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
    CrossRef
  11. Choi J, Trinh TX, 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)
    CrossRef
  12. 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
    CrossRef
  13. 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
    CrossRef
  14. 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;
    CrossRef
  15. Lee DH, 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
    CrossRef
  16. 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
    CrossRef
  17. 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)
    CrossRef
  18. 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 2020;
    CrossRef
  19. Keim-Malpass J, Clark MT, Lake DE, Moorman JR. Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. Journal of Clinical Monitoring and Computing 2020;34(4):797
    CrossRef
  20. Lhommet C, Garot D, Grammatico-Guillon L, Jourdannaud C, Asfar P, Faisy C, Muller G, Barker KA, 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)
    CrossRef
  21. Bulgarelli L, Deliberato RO, Johnson AE. Prediction on critically ill patients: The role of “big data”. Journal of Critical Care 2020;60:64
    CrossRef
  22. Ibrahim ZM, Wu H, Hamoud A, Stappen L, Dobson RJB, Agarossi A. On classifying sepsis heterogeneity in the ICU: insight using machine learning. Journal of the American Medical Informatics Association 2020;27(3):437
    CrossRef
  23. 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;
    CrossRef
  24. Opal SM, Wittebole X. Biomarkers of Infection and Sepsis. Critical Care Clinics 2020;36(1):11
    CrossRef
  25. 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
    CrossRef
  26. 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
    CrossRef
  27. Piccolo SR, Lee TJ, Suh E, Hill K. ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data. GigaScience 2020;9(4)
    CrossRef
  28. Kasson PM. Infectious Disease Research in the Era of Big Data. Annual Review of Biomedical Data Science 2020;3(1):43
    CrossRef
  29. Tang S, Chappell GT, Mazzoli A, Tewari M, Choi SW, 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
    CrossRef
  30. Park HJ, Jung DY, 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
    CrossRef
  31. Cinel I, Kasapoglu US, Gul F, Dellinger RP. The initial resuscitation of septic shock. Journal of Critical Care 2020;57:108
    CrossRef
  32. Barbour K, Hesdorffer DC, Tian N, Yozawitz EG, McGoldrick PE, Wolf S, McDonough TL, Nelson A, Loddenkemper T, Basma N, Johnson SB, Grinspan ZM. Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing. Epilepsia 2019;
    CrossRef
  33. Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, Balamuth F, Schmatz M, Grundmeier RW, Juarez JM. 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
    CrossRef
  34. Saber H, Somai M, Rajah GB, Scalzo F, Liebeskind DS. Predictive analytics and machine learning in stroke and neurovascular medicine. Neurological Research 2019;41(8):681
    CrossRef
  35. 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
    CrossRef
  36. Kindle RD, Badawi O, Celi LA, 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
    CrossRef
  37. 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
    CrossRef
  38. 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
    CrossRef
  39. Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Survey of Ophthalmology 2019;64(2):233
    CrossRef
  40. Le S, Hoffman J, Barton C, Fitzgerald JC, Allen A, Pellegrini E, Calvert J, Das R. Pediatric Severe Sepsis Prediction Using Machine Learning. Frontiers in Pediatrics 2019;7
    CrossRef
  41. Ginestra JC, Giannini HM, Schweickert WD, Meadows L, Lynch MJ, Pavan K, Chivers CJ, Draugelis M, Donnelly PJ, Fuchs BD, Umscheid CA. 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
    CrossRef
  42. Amland RC, Burghart M, Overhage JM. Sepsis surveillance: an examination of parameter sensitivity and alert reliability. JAMIA Open 2019;2(3):339
    CrossRef
  43. Pirracchio R, Cohen MJ, 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
    CrossRef
  44. López-Martínez F, Núñez-Valdez ER, Lorduy Gomez J, García-Díaz V. A neural network approach to predict early neonatal sepsis. Computers & Electrical Engineering 2019;76:379
    CrossRef
  45. 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
    CrossRef
  46. Ruminski CM, Clark MT, Lake DE, Kitzmiller RR, Keim-Malpass J, Robertson MP, Simons TR, Moorman JR, Calland JF. 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
    CrossRef
  47. 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
    CrossRef
  48. 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)
    CrossRef
  49. Rush B, Celi LA, Stone DJ. Applying machine learning to continuously monitored physiological data. Journal of Clinical Monitoring and Computing 2019;33(5):887
    CrossRef
  50. 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
    CrossRef
  51. Delahanty RJ, Alvarez J, Flynn LM, Sherwin RL, Jones SS. 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
    CrossRef
  52. 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
    CrossRef
  53. Eguia E, Cobb AN, Baker MS, Joyce C, Gilbert E, Gonzalez R, Afshar M, Churpek MM. Risk factors for infection and evaluation of Sepsis-3 in patients with trauma. The American Journal of Surgery 2019;218(5):851
    CrossRef
  54. 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
    CrossRef
  55. Yee CR, Narain NR, Akmaev VR, Vemulapalli V. A Data-Driven Approach to Predicting Septic Shock in the Intensive Care Unit. Biomedical Informatics Insights 2019;11:117822261988514
    CrossRef
  56. 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
    CrossRef
  57. Rahman MA, Tumian A. Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review. Applied Mechanics and Materials 2019;892:274
    CrossRef
  58. 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
    CrossRef
  59. Vázquez-López R, Rivero Rojas O, Ibarra Moreno A, Urrutia Favila JE, Peña Barreto A, Ortega Ortuño GL, Abello Vaamonde JA, Aguilar Velazco IA, Félix Castro JM, Solano-Gálvez SG, Barrientos Fortes T, González-Barrios JA. Antibiotic-Resistant Septicemia in Pediatric Oncology Patients Associated with Post-Therapeutic Neutropenic Fever. Antibiotics 2019;8(3):106
    CrossRef
  60. Lovejoy CA, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. Critical Care 2019;23(1)
    CrossRef
  61. 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
    CrossRef
  62. Davoodi R, Moradi MH. Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. Journal of Biomedical Informatics 2018;79:48
    CrossRef
  63. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine 2018;46(4):547
    CrossRef
  64. Pruinelli L, Westra BL, Yadav P, Hoff A, Steinbach M, Kumar V, Delaney CW, 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
    CrossRef
  65. 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
    CrossRef
  66. Hong WS, Haimovich AD, Taylor RA, Dong Q. Predicting hospital admission at emergency department triage using machine learning. PLOS ONE 2018;13(7):e0201016
    CrossRef
  67. Kulabukhov VV, Kudryavtsev AN, Kleuzovich AA, Chizhov AG, Raevskaya MB. Diagnostic value of molecular biomarkers of infection in screening by Sepsis-3 criteria. Khirurgiya. Zhurnal im. N.I. Pirogova 2018;(5):58
    CrossRef
  68. Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A, Ashworth S, Beale R, Brett SJ, Singer M, Ercole A, Celi LA. Optimal intensive care outcome prediction over time using machine learning. PLOS ONE 2018;13(11):e0206862
    CrossRef
  69. Mohamadlou H, Lynn-Palevsky A, Barton C, Chettipally U, Shieh L, Calvert J, Saber NR, 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;5:205435811877632
    CrossRef
  70. Weldon DL, 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
    CrossRef
  71. Lee H, Jung C. Anesthesia research in the artificial intelligence era. Anesthesia and Pain Medicine 2018;13(3):248
    CrossRef
  72. Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI. Emerging Technologies for Molecular Diagnosis of Sepsis. Clinical Microbiology Reviews 2018;31(2)
    CrossRef
  73. Woods JS, Saxena M, Nagamine T, Howell RS, Criscitelli T, Gorenstein S, M. Gillette B. The Future of Data-Driven Wound Care. AORN Journal 2018;107(4):455
    CrossRef
  74. 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
    CrossRef
  75. 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
    CrossRef
  76. Shashikumar SP, Stanley MD, Sadiq I, Li Q, Holder A, Clifford GD, Nemati S. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. Journal of Electrocardiology 2017;50(6):739
    CrossRef
  77. 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
    CrossRef
  78. Shashikumar SP, Li Q, Clifford GD, Nemati S. Multiscale network representation of physiological time series for early prediction of sepsis. Physiological Measurement 2017;38(12):2235
    CrossRef
  79. Park E, Chang H, Nam HS. Use of Machine Leaning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients. Journal of Medical Internet Research 2017;19(4):e120
    CrossRef
  80. Das R, Wales DJ. Machine learning landscapes and predictions for patient outcomes. Royal Society Open Science 2017;4(7):170175
    CrossRef
  81. Salluh JIF, Soares M, Keegan MT. Understanding intensive care unit benchmarking. Intensive Care Medicine 2017;43(11):1703
    CrossRef
  82. 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
    CrossRef
  83. 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
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/medinform.5909)

:
  1. Bulgarelli L, Núñez-Reiz A, Deliberato RO. Leveraging Data Science for Global Health. 2020. Chapter 4:55
    CrossRef
  2. Thomas MA, Abraham DS, Liu D. Interdisciplinary Approaches to Digital Transformation and Innovation. 2020. chapter 6:123
    CrossRef
  3. Berikol GB, Berikol G. Artificial Intelligence in Precision Health. 2020. :405
    CrossRef
  4. Pérez-Fernández J, Raimondi NA, Murillo Cabezas F. Critical Care Administration. 2020. Chapter 9:111
    CrossRef
  5. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. 2019. :117
    CrossRef
  6. Xie J, Coopersmith CM. Handbook of Sepsis. 2018. Chapter 16:253
    CrossRef