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

Secondary Use of Clinical Data for Research and Surveillance (367)

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 (36) Tweetations (7) Metrics

Journals

  1. Gillies C, Taylor D, Cummings B, Ansari S, Islim F, Kronick S, Medlin R, Ward K. Demonstrating the consequences of learning missingness patterns in early warning systems for preventative health care: A novel simulation and solution. Journal of Biomedical Informatics 2020;110:103528 View
  2. Smith C, Hendrickson A, Grudem M, Klampe C, Deering E, Jatoi A. Loratadine for Paclitaxel-Induced Myalgias and Arthralgias. American Journal of Hospice and Palliative Medicine® 2020;37(3):235 View
  3. Sperrin M, Martin G, Sisk R, Peek N. Missing data should be handled differently for prediction than for description or causal explanation. Journal of Clinical Epidemiology 2020;125:183 View
  4. Futoma J, Simons M, Panch T, Doshi-Velez F, Celi L. The myth of generalisability in clinical research and machine learning in health care. The Lancet Digital Health 2020;2(9):e489 View
  5. Leisman D, Harhay M, Lederer D, Abramson M, Adjei A, Bakker J, Ballas Z, Barreiro E, Bell S, Bellomo R, Bernstein J, Branson R, Brusasco V, Chalmers J, Chokroverty S, Citerio G, Collop N, Cooke C, Crapo J, Donaldson G, Fitzgerald D, Grainger E, Hale L, Herth F, Kochanek P, Marks G, Moorman J, Ost D, Schatz M, Sheikh A, Smyth A, Stewart I, Stewart P, Swenson E, Szymusiak R, Teboul J, Vincent J, Wedzicha J, Maslove D. Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals. Critical Care Medicine 2020;48(5):623 View
  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
  8. Sisk R, Lin L, Sperrin M, Barrett J, Tom B, Diaz-Ordaz K, Peek N, Martin G. Informative presence and observation in routine health data: A review of methodology for clinical risk prediction. Journal of the American Medical Informatics Association 2021;28(1):155 View
  9. O’Halloran H, Kwong K, Veldhoen R, Maslove D. Characterizing the Patients, Hospitals, and Data Quality of the eICU Collaborative Research Database*. Critical Care Medicine 2020;48(12):1737 View
  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
  11. Seo D, Yi H, Bae H, Kim Y, Sohn C, Ahn S, Lim K, Kim N, Kim W. Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach. Journal of Clinical Medicine 2021;10(5):1089 View
  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
  13. Futoma J, Simons M, Doshi-Velez F, Kamaleswaran R. Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables. Critical Care Explorations 2021;3(7):e0453 View
  14. Zhang H, Yi D, Guan Y. Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records. STAR Protocols 2021;2(3):100639 View
  15. Zhou Y, Saghapour E. ImputEHR: A Visualization Tool of Imputation for the Prediction of Biomedical Data. Frontiers in Genetics 2021;12 View
  16. 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
  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
  24. Old O, Friedrichson B, Zacharowski K, Kloka J. Entering the new digital era of intensive care medicine: an overview of interdisciplinary approaches to use artificial intelligence for patients’ benefit. European Journal of Anaesthesiology Intensive Care 2023;2(1):e0014 View
  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
  27. Sun Y, Zhou Y. A Machine Learning Pipeline for Mortality Prediction in the ICU. International Journal of Digital Health 2022;2(1):3 View
  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
  31. Wu T, Vernooij L, Duprey M, Zaal I, Gélinas C, Devlin J, Slooter A. Relationship Between Pain and Delirium in Critically Ill Adults. Critical Care Explorations 2023;5(12):e1012 View
  32. Pham M, Mai T, Crane M, Ebiele M, Brennan R, Ward M, Geary U, McDonald N, Bezbradica M. Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models. Diagnostics 2024;14(21):2405 View

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