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This research will develop and evaluate a semi-automatic approach to conducting literature searches for systematic reviews.
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 project will use Learning Health System methods to systematically apply U.S. Preventive Services Task Force’s evidence-based recommendations with the goal of advancing individualized precision prevention.
The central goal of the annual Conference on Health IT & Analytics is to develop a health information technology and analytics (HIT+A) research agenda that supports national efforts to create a learning health system that produces evidence to make health care safer, of higher quality, more accessible, equitable, and affordable.
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 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.
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.