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

Secondary Use of Clinical Data for Research and Surveillance (375)

Published on in Vol 7, No 1 (2019): Jan-Mar

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

Authors of this article:

Anis Sharafoddini1 Author Orcid Image ;   Joel A Dubin1, 2 Author Orcid Image ;   David M Maslove3 Author Orcid Image ;   Joon Lee1, 4, 5 Author Orcid Image
Article Authors Cited by (37) Tweetations (7) Metrics

Journals

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  6. Sharafoddini A, Dubin J, Lee J. Identifying subpopulations of septic patients: A temporal data-driven approach. Computers in Biology and Medicine 2021;130:104182 View
  7. Maslove D, Elbers P, Clermont G. Artificial intelligence in telemetry: what clinicians should know. Intensive Care Medicine 2021;47(2):150 View
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  10. Ronzio L, Cabitza F, Barbaro A, Banfi G. Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine. Diagnostics 2021;11(2):372 View
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  12. Abad Z, Maslove D, Lee J. Predicting Discharge Destination of Critically Ill Patients Using Machine Learning. IEEE Journal of Biomedical and Health Informatics 2021;25(3):827 View
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  17. Singh J, Sato M, Ohkuma T. On Missingness Features in Machine Learning Models for Critical Care: Observational Study. JMIR Medical Informatics 2021;9(12):e25022 View
  18. Perez-Lebel A, Varoquaux G, Le Morvan M, Josse J, Poline J. Benchmarking missing-values approaches for predictive models on health databases. GigaScience 2022;11 View
  19. 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
  20. Bose S, Greenstein J, Fackler J, Sarma S, Winslow R, Bembea M. Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit. Frontiers in Pediatrics 2021;9 View
  21. Saffari S, Volovici V, Ong M, Goldstein B, Vaughan R, Dammers R, Steyerberg E, Liu N. Proper Use of Multiple Imputation and Dealing with Missing Covariate Data. World Neurosurgery 2022;161:284 View
  22. Sharma V, KULKARNI V, MCALISTER F, EURICH D, KESHWANI S, SIMPSON S, VOAKLANDER D, SAMANANI S. Predicting 30-Day Readmissions in Patients With Heart Failure Using Administrative Data: A Machine Learning Approach. Journal of Cardiac Failure 2022;28(5):710 View
  23. Steif J, Brant R, Sreepada R, West N, Murthy S, Görges M. Prediction Model Performance With Different Imputation Strategies: A Simulation Study Using a North American ICU Registry. Pediatric Critical Care Medicine 2022;23(1):e29 View
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  25. Wiegand M, Cowan S, Waddington C, Halsall D, Keevil V, Tom B, Taylor V, Gkrania-Klotsas E, Preller J, Goudie R. Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study. BMJ Open 2022;12(9):e060026 View
  26. Shi Z, Wang S, Yue L, Pang L, Zuo X, Zuo W, Li X. Deep dynamic imputation of clinical time series for mortality prediction. Information Sciences 2021;579:607 View
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  28. Lee J, Hauskrecht M. Personalized event prediction for Electronic Health Records. Artificial Intelligence in Medicine 2023;143:102620 View
  29. Sharma V, Kulkarni V, Joon T, Eurich D, Simpson S, Voaklander D, Wright B, Samanani S. Predicting falls-related admissions in older adults in Alberta, Canada: a machine-learning falls prevention tool developed using population administrative health data. BMJ Open 2023;13(8):e071321 View
  30. Sharma V, Joon T, Kulkarni V, Samanani S, Simpson S, Voaklander D, Eurich D. Predicting 30-day risk from benzodiazepine/Z-drug dispensations in older adults using administrative data: A prognostic machine learning approach. International Journal of Medical Informatics 2023;178:105177 View
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Books/Policy Documents

  1. Mikalsen K, Soguero-Ruiz C, Jenssen R. Explainable AI in Healthcare and Medicine. View
  2. Cinquini M, Giannotti F, Guidotti R, Mattei A. Explainable Artificial Intelligence. View