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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

Journals

  1. Yong X, Kang T, Li M, Li S, Yan X, Li J, Lin J, Lu B, Zheng J, Xu Z, Yang Q, Li J. Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning. Mammalian Genome 2025;36(1):183 View
  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
  4. Jin Y, Zhao M, Su T, Fan Y, Ouyang Z, Lv F. Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study. Journal of Medical Internet Research 2025;27:e69864 View
  5. Pan Y, Tian S, Guo J, Cai H, Wan J, Fang C. Clinical feasibility of AI Doctors: Evaluating the replacement potential of large language models in outpatient settings for central nervous system tumors. International Journal of Medical Informatics 2025;203:106013 View
  6. Sahu D, Ghosh S, Jayaraman S, Neelapu B, Pal K. Low-cost machine learning-integrated optical spectrophotometer for non-destructive color and shelf-life analysis: A study on sliced bread. Food Chemistry 2025;492:145379 View
  7. Line T, Chinthala A, Obeng-Gyasi B, Mao G, Bradbury J, Mittal A, Vargas J, Kellogg R, Nwachuku E, Okonkwo D, Pease M. Comparison of Machine Learning Methods to Predict Early Mortality After Evacuation of Chronic Subdural Hematoma. Neurosurgery Practice 2025;6(3) View
  8. Liu J, Zhang L, Yuan Y, Tang J, Liu Y, Xia L, Zhang J. Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation. JMIR Medical Informatics 2025;13:e75665 View
  9. Wu H, Lv X, Yang J, Lei D, Wan S, Bai C, Zhuang S, Wang B, Wei D, Long X, Xue H, Zhang X, Fan X, Huang L, Tian Z, Li M, Wang H, Yin X. CT-Based Automated Segmentation and Recurrence Prediction in Chronic Subdural Hematoma: A Dual-Label Multicenter Study. Journal of Neurotrauma 2026 View
  10. Wang X, Sun Y, Xu X, Li M, Gao L, Hu Y, Wang W. Construction of an Intelligent Decision-Making Model for Laparoscopic Common Bile Duct Repair. Journal of Laparoendoscopic & Advanced Surgical Techniques 2026 View

Conference Proceedings

  1. Kumar M, Pandey S, Bania S, Khandelwal P, Roy D, Ghosh T. 2025 2nd International Conference on Software, Systems and Information Technology (SSITCON). Integrative Machine Learning for Early Diagnosis and Prognosis of Cystic View