معرفی
Daniel Fabbri is an Assistant Professor of Biomedical Informatics in the School of Medicine and Assistant Professor of Computer Science in the School of Engineering at Vanderbilt University. His research focuses on database systems and machine learning applied to electronic medical records (EHRs) and clinical data. Notably, he developed the Explanation-Based Auditing System, which uses data mining to monitor EHR access and identify inappropriate use. He holds a Ph.D. in Computer Science from the University of Michigan and a B.S. in Computer Science and Engineering from UCLA. His work has been recognized with a National Science Foundation Innovation Corps award for commercializing auditing technology at Maize Analytics.
Research interests include healthcare data security, clinical decision support systems, and applying AI to improve patient care. He has explored NLP applications for phenotyping chronic conditions, predicting treatment outcomes, and analyzing social media health mentions. His recent projects address challenges in postmarketing drug surveillance, surgical risk prediction, and sleep apnea diagnostics.
- Education: Ph.D., Computer Science (University of Michigan); B.S., Computer Science and Engineering (UCLA)
- Awards: NSF Innovation Corps award for Maize Analytics
- Key Projects: Auditing systems, EHR search engines, immune therapy outcome prediction
