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Citing this Article

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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. 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
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  2. 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 2019;:146045821985287
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  3. Kapoor R, Walters SP, Al-Aswad LA. The current state of artificial intelligence in ophthalmology. Survey of Ophthalmology 2019;64(2):233
    CrossRef
  4. 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
  5. 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 2019;
    CrossRef
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. Lovejoy CA, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. Critical Care 2019;23(1)
    CrossRef
  17. Schinkel M, Paranjape K, Panday RN, Skyttberg N, Nanayakkara P. Clinical applications of artificial intelligence in sepsis: A narrative review. Computers in Biology and Medicine 2019;:103488
    CrossRef
  18. 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
  19. 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
  20. Amland RC, Burghart M, Overhage JM. Sepsis surveillance: an examination of parameter sensitivity and alert reliability. JAMIA Open 2019;
    CrossRef
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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;
    CrossRef
  29. 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
  30. 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
  31. 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
  32. Lee H, Jung C. Anesthesia research in the artificial intelligence era. Anesthesia and Pain Medicine 2018;13(3):248
    CrossRef
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. Das R, Wales DJ. Machine learning landscapes and predictions for patient outcomes. Royal Society Open Science 2017;4(7):170175
    CrossRef
  48. 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
  49. 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
  50. 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
  51. Salluh JIF, Soares M, Keegan MT. Understanding intensive care unit benchmarking. Intensive Care Medicine 2017;43(11):1703
    CrossRef

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

:
  1. Cerrato P, Halamka J. The Transformative Power of Mobile Medicine. 2019. :117
    CrossRef
  2. Xie J, Coopersmith CM. Handbook of Sepsis. 2018. Chapter 16:253
    CrossRef