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 developed, refined, and assessed the validity of methods for handling missing data in distributed data networks, without sharing individual-level data, to improve data quality and research validity.
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 compare the use of predictive modeling versus traditional questionnaires to identify those with unmet social needs, use the superior method to inform the development of a clinical decision support tool, and evaluate the tool’s impact on referrals to social providers.
This study created, trained, and tested machine learning (ML) algorithms to predict emergency department returns and morbidity or mortality among returns for COVID-19 patients, with results showing ML’s potential to inform clinicians on whether admission to the hospital may be needed for the prevention of complications and to prioritize resources for higher risk patients.
This research will use digital health tools leveraging patient-reported outcomes and data from electronic health records to engage individuals with multiple chronic conditions to improve understanding of individualized risk of adverse events during care transitions.
