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

Artificial Intelligence (1543) Clinical Information and Decision Making (1506) Machine Learning (1581) Data Science (288) Pulmonology, COPD (151) Respiratory Medicine (99) Secondary Use of Clinical Data for Research and Surveillance (387)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48595, first published .
Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study

Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study

Authors of this article:

Ori Ben Yehuda1 Author Orcid Image ;   Edward Itelman2, 3 Author Orcid Image ;   Adva Vaisman2 Author Orcid Image ;   Gad Segal2 Author Orcid Image ;   Boaz Lerner1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Yamagishi Y, Nakamura Y, Hanaoka S, Abe O. Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation. JMIR Cancer 2025;11:e57275 View
  2. Lima L, Silva I, Sanglard B, Sanglard P, Lima G, Almeida Â, Moura H, Souza H, Santos J, Verbo M, Lemos M, Madruga S, Borges G, Nascimento M, Sousa A. ABORDAGENS CONTEMPORÂNEAS NO GERENCIAMENTO DA TROMBOEMBOLIA PULMONAR E COMPLICAÇÕES: INTEGRAÇÃO DE ESTRATÉGIAS FARMACOLÓGICAS, INTERVENCIONISTAS E MULTIDISCIPLINARES. Revista Contemporânea 2025;5(2):e7436 View