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

Viewpoints and Perspectives (425) Artificial Intelligence (829) Machine Learning (1122) Clinical Informatics (660) Secondary Use of Clinical Data for Research and Surveillance (266) Health Care Quality and Health Services Research (165) Health Services Research (286)

Published on in Vol 23 , No 12 (2021) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29812, first published .
Analyzing Patient Trajectories With Artificial Intelligence

Analyzing Patient Trajectories With Artificial Intelligence

Analyzing Patient Trajectories With Artificial Intelligence

Authors of this article:

Ahmed Allam 1, 2 Author Orcid Image ;   Stefan Feuerriegel 3, 4, 5 Author Orcid Image ;   Michael Rebhan 3 Author Orcid Image ;   Michael Krauthammer 1, 2, 6 Author Orcid Image
Article Authors Cited by (3) Tweetations (25) Metrics

Journals

  1. Schallmoser S, Zueger T, Kraus M, Saar-Tsechansky M, Stettler C, Feuerriegel S. Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study. Journal of Medical Internet Research 2023;25:e42181 View
  2. Naumzik C, Feuerriegel S, Nielsen A. Data-driven dynamic treatment planning for chronic diseases. European Journal of Operational Research 2023;305(2):853 View
  3. Kovács A, Tokodi M. Refining Echocardiographic Surveillance of Aortic Stenosis Using Machine Learning. JACC: Cardiovascular Imaging 2023 View