Year: 2026
Type: article
Abstract: Accurate identification of acute and subacute subdural hematoma (acute/subacute SDH) is critical for improved patient outcomes. However, large-scale research is hindered by unreliable identification methods in electronic health records (EHRs). Current approaches relying on International Classification of Diseases (ICD) codes lack specificity and cannot distinguish acute, subacute, and chronic cases; manual chart review is too labor-intensive to scale. We developed an automated phenotyping algorithm using structured data and unstructured clinical notes for high-accuracy retrospective identification of acute/subacute SDH. We analyzed 2999 records from two hospitals, including ICD-positive and ICD-negative acute/subacute SDH cases verified by manual chart review. Features for model training included ICD codes, Current Procedural Terminology (CPT) codes, and clinical note keywords. Logistic regression and random forest models were trained using cross-validation and evaluated using AUROC and AUPRC. External validation involved training on one hospital and testing on the other. The random forest keywords-only model performed best, achieving an AUROC of 0.985 (95% CI: 0.980–0.990) and AUPRC of 0.944 (95% CI: 0.923–0.962) on the test set. External validation demonstrated strong AUROCs of 0.965 and 0.971 and AUPRCs of 0.831 and 0.840. The overall error rate was <1%. This model provides a scalable, highly accurate approach to acute/subacute SDH detection in EHR research.
Source: Algorithms
DOI: 10.3390/a19030239
Authors: Gregory B. Hooke, Haoqi Sun, Catherine Clive, Spencer Boris, Niels Turley, Lydia Petersen, Jaden Searle, Bram Overmeer, Ali Han Yaramis, Karan Singh, Arjun Singh, Daniel Sumsion, Aditya Gupta, Manohar Ghanta, Valdery F. Moura Junior, Marta Fernandes, Katie L. Stone, Dennis Hwang, Lynn Marie Trotti, Gari D. Clifford, Umakanth Katwa, Shibani S. Mukerji, Sahar F. Zafar, Robert J. Thomas, M. Brandon Westover
Institutions: Brigham Young University, Beth Israel Deaconess Medical Center, University of Groningen, Massachusetts General Hospital, University of California, San Francisco, California Pacific Medical Center, Kaiser Permanente, Kaiser Permanente Fontana Medical Center, Emory University, Georgia Institute of Technology, Boston Children's Hospital
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Awards: G1441292203, Comparative effectiveness of EEG-guided anti-seizure treatment in acute brain injury, Establishing a Brain Health Index, G580858672, G6315715174, Late-onset Unexplained Epilepsy as a Risk Factor for Dementia, G7664069821, Integrative Motor Activity Biomarker for the Risk of Alzheimer's Risk, G9000066320, G9000081834, Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality, Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain Injury
OpenAlex ID: W7140121107