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AHRQ-Funded Projects
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- Remove Machine Learning filter Machine Learning
Search found 17 items
Semi-Automated Identification of Biomedical Literature
This research will develop and evaluate a semi-automatic approach to conducting literature searches for systematic reviews.
Improving Missing Data Analysis in Distributed Research Networks
This project aims to refine and develop methods to address missing electronic health record data to improve data quality and research validity.
TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in ARDS
This project will develop and evaluate an electronic clinical decision support tool for care of patients with Acute Respiratory Distress Syndrome.
Achieving Individualized Precision Prevention (IPP) through Scalable Infrastructure Employing the USPSTF Recommendations in Computable Form
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.
Annual Conference on Health Information Technology & Analytics (CHITA)
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.
Improving Diabetes and Depression Self-management Via Adaptive Mobile Messaging
This project will develop and test a personalized motivational text messaging intervention to improve management of diabetes and depression in low-income populations.
Enabling Large-Scale Research on Autism Spectrum Disorders Through Automated Processing of EHR Using Natural Language Understanding
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.
Anesthesiology Control Tower: Feedback Alerts to Supplement Treatment (ACTFAST)
The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients
This research prospectively evaluated a machine learning algorithm that identifies candidates for neurologic surgery to control epilepsy.
Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation
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.
