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This research will develop and evaluate an artificial intelligence-driven clinical decision support system to detect and manage acute kidney injury in the emergency department.
This research will develop and evaluate a semi-automatic approach to conducting literature searches for systematic reviews.
This research will enhance and test a tool which uses natural language processing to provide a real-time assessment of dialog during motivational interviewing training.
This project will create and evaluate a learning network to expand the use of the Rheumatology Informatics System for Effectiveness (RISE) registry for patient-reported outcomes on rheumatoid arthritis.
This project will redesign approaches for collecting and using allergy information with the goal of improving healthcare quality and safety, including completeness and accuracy of allergy data.
This project will develop and test a personalized motivational text messaging intervention to improve management of diabetes and depression in low-income populations.
This project will develop and disseminate an innovative communication system to identify and mitigate health risks for young African American women before pregnancy as a means of reducing health disparities in birth outcomes.
This project will design natural language processing algorithms to extract data from free text notes on autism spectrum disorders in electronic health records, and demonstrate the feasibility and usefulness of this approach.
The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.