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

Journals
- 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
- 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
- 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
- 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
- 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
- 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
- Maslove D, Elbers P, Clermont G. Artificial intelligence in telemetry: what clinicians should know. Intensive Care Medicine 2021;47(2):150 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zhou Y, Saghapour E. ImputEHR: A Visualization Tool of Imputation for the Prediction of Biomedical Data. Frontiers in Genetics 2021;12 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Sun Y, Zhou Y. A Machine Learning Pipeline for Mortality Prediction in the ICU. International Journal of Digital Health 2022;2(1):3 View
- Lee J, Hauskrecht M. Personalized event prediction for Electronic Health Records. Artificial Intelligence in Medicine 2023;143:102620 View
- 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
- 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
- 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
- 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
- Nanini S, Abid M, Mamouni Y, Wiedemann A, Jouvet P, Bourassa S. Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies. Diagnostics 2024;14(23):2763 View
- Ren W, Fan K, Liu Z, Wu Y, An H, Liu H. Overcoming Missing Data: Accurately Predicting Cardiovascular Risk in Type 2 Diabetes, A Systematic Review. Journal of Diabetes 2025;17(1) View
- Ehrig M, Bullock G, Leng X, Pajewski N, Speiser J. Imputation and Missing Indicators for Handling Missing Longitudinal Data: Data Simulation Analysis Based on Electronic Health Record Data. JMIR Medical Informatics 2025;13:e64354 View
- Yilmaz Başer H, Evran T, Cifci M. Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models. Biomedicines 2025;13(6):1449 View
- Shi J, Hubbard A, Fong N, Pirracchio R. Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables. BMC Medical Informatics and Decision Making 2025;25(1) View
- Abdukhakimova D, Beyembetova A, Ablayeva A, Akhmedullin R, Aimyshev T, Biniyazova A, Zhakhina G, Orazbekova Z, Orazova G, Gaipov A. Epidemiological Insights into Colorectal Cancer Survival in Kazakhstan (2014–2023): A Retrospective Analysis Using the National Electronic Registry of Oncological Patients. Cancers 2025;17(14):2336 View
- Li D, Yu A, Fuhrman D, Liu M. SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury. Journal of the American Medical Informatics Association 2025 View
Books/Policy Documents
Conference Proceedings
- Danilatou V, Antonakaki D, Tzagkarakis C, Kanterakis A, Katos V, Kostoulas T. 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). Automated Mortality Prediction in Critically-ill Patients with Thrombosis using Machine Learning View
- Van Ness M, Bosschieter T, Halpin-Gregorio R, Udell M. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The Missing Indicator Method: From Low to High Dimensions View
- Liu Y, Zhang W, Si J, Li Z, Peng X, Lu W. 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Generating Personalized Imputations for Patient Health Status Prediction in Electronic Health Records View