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

Clinical Information and Decision Making (1548) Artificial Intelligence (1602) Machine Learning (1627) Postoperative Monitoring and Telemonitoring (148)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54944, first published .
Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study

Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study

Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study

Authors of this article:

Cheng Fang1 Author Orcid Image ;   Xiao Ji2 Author Orcid Image ;   Yifeng Pan3 Author Orcid Image ;   Guanchao Xie4 Author Orcid Image ;   Hongsheng Zhang4 Author Orcid Image ;   Sai Li4 Author Orcid Image ;   Jinghai Wan1, 4 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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  2. Guranda A, Richter A, Wach J, Güresir E, Vychopen M. PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy. Brain Sciences 2025;15(1):58 View
  3. Kota N, Keshireddy A, Pruthi A, Abidin Z, Koneru M. A Scoping Review of the Methodologies and Reporting Standards in Recent Applications of Artificial Intelligence in Radiomics for Chronic Subdural Hematoma Imaging. Cureus 2025 View