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.

Journal: Journal of medical Internet research

Published Date: Jul 30, 2024

Abstract

BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' lives-is a major challenge confronting modern medicine.

Authors

Ori Ben Yehuda

Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Edward Itelman

Education Authority, Chaim Sheba Medical Center, Faculty of Health Science and Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Adva Vaisman

Education Authority, Chaim Sheba Medical Center, Faculty of Health Science and Medicine, Tel-Aviv University, Tel-Aviv, Israel.
Gad Segal

Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Boaz Lerner

Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, POB 653, Beer Sheva, Israel.

Keywords

Adult Aged Early Diagnosis Female Hospitalization Humans Machine Learning Male Middle Aged Patient Admission Pulmonary Embolism Risk Factors

External Resources

View on PubMed Access via DOI PubMed (39079116)

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.

Abstract

Authors

Keywords

External Resources

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