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

Machine Learning (1465) Clinical Informatics (1022) Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1186)

Published on in Vol 8, No 3 (2020): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16117, first published .
Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Predicting Adverse Outcomes for Febrile Patients in the Emergency Department Using Sparse Laboratory Data: Development of a Time Adaptive Model

Authors of this article:

Sungjoo Lee1 Author Orcid Image ;   Sungjun Hong1 Author Orcid Image ;   Won Chul Cha1, 2, 3 Author Orcid Image ;   Kyunga Kim1, 4 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. De Bruyne S, Speeckaert M, Van Biesen W, Delanghe J. Recent evolutions of machine learning applications in clinical laboratory medicine. Critical Reviews in Clinical Laboratory Sciences 2021;58(2):131 View
  2. 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
  3. Li C, Zhang Z, Ren Y, Nie H, Lei Y, Qiu H, Xu Z, Pu X. Machine learning based early mortality prediction in the emergency department. International Journal of Medical Informatics 2021;155:104570 View
  4. Sadegh-Zadeh S, Sakha H, Movahedi S, Fasihi Harandi A, Ghaffari S, Javanshir E, Ali S, Hooshanginezhad Z, Hajizadeh R. Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification. Computers in Biology and Medicine 2023;167:107696 View