Search found 5 items
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.
This research took an existing sepsis-related clinical decision support (CDS) and developed, tested, implemented, and validated a knowledge-based artificial intelligence-enhanced sepsis CDS.
This research studied errors in medical documents created with speech recognition software and developed natural language processing methods to detect such errors.