Published on in Vol 7, No 9 (2018): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9466, first published .
Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

Journals

  1. Romero-Brufau S, Wyatt K, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital. Applied Clinical Informatics 2020;11(04):570 View
  2. 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 View
  3. Rippe W, Dittberner A, Boeger D, Buentzel J, Hoffmann K, Kaftan H, Mueller A, Radtke G, Guntinas-Lichius O, Dziegielewski P. 30-day unplanned readmission rate in otolaryngology patients: A population-based study in Thuringia, Germany. PLOS ONE 2019;14(10):e0224146 View
  4. Lv H, Yang X, Wang B, Wang S, Du X, Tan Q, Hao Z, Liu Y, Yan J, Xia Y. Machine Learning–Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study. Journal of Medical Internet Research 2021;23(4):e24996 View