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Red blood cell (RBC) transfusion is a common medical procedure. While it offers clinical benefits for many, hemodynamically stable patients are often subjected to unwarranted transfusions, with the potential to lead to adverse consequences. We created a real-time clinical decision support (CDS) tool in the electronic health record system to address this problem and optimize transfusion practice as part of an institutional multidisciplinary, team-based patient blood management program.

Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening [...]

The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH.

Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.

To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.

Fertility is becoming increasingly supported by consumer health technologies, especially mobile apps that support self-tracking activities. However, it is not clear whether the apps support the variety of goals and life events of those who menstruate, especially during transitions between them.

Concerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner.

While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective.