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

Non-randomized Protocols and Methods (ehealth) (568)

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

Authors of this article:

Sujay Kakarmath1, 2, 3 Author Orcid Image ;   Sara Golas1 Author Orcid Image ;   Jennifer Felsted1, 3 Author Orcid Image ;   Joseph Kvedar2, 3, 4 Author Orcid Image ;   Kamal Jethwani2, 3, 4 Author Orcid Image ;   Stephen Agboola1, 2, 3 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

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  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
  5. Lotierzo M, Bruno R, Finan-Marchi A, Huet F, Kalmanovich E, Rodrigues G, Dupuy A, Adda J, Piquemal D, Richard S, Cristol J, Roubille F. Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?. Medicina 2021;57(10):996 View
  6. Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022;10(9):2188 View
  7. Kwon O, Na W, Kang H, Jun T, Kweon J, Park G, Cho Y, Hur C, Chae J, Kang D, Lee P, Ahn J, Park D, Kang S, Lee S, Lee C, Park S, Park S, Yang D, Kim Y. Electronic Medical Record–Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation. JMIR Medical Informatics 2022;10(5):e26801 View
  8. Daye D, Parker R, Tripathi S, Cox M, Brito Orama S, Valentin L, Bridge C, Uppot R. CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology. Cancers 2024;16(11):1975 View