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It is unclear to what extent simulated versions of real data can be used to assess potential value of new biomarkers added to prognostic risk models. Using data on 4522 women and 3969 men who contributed information to the Framingham CVD risk prediction tool, we develop a simulation model that allows assessment of the added contribution of new biomarkers. The simulated model matches closely the one obtained using real data [...]

Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features.

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

This quality improvement project evaluated the impact of a tailored, evidence-based training strategy on advanced electronic medical record (EMR) use for Veterans Administration (VA) clinicians experienced in using the EMR. After developing the curriculum, an online needs assessment tool evaluated 20 clinicians' competency gaps. Responses were used to prioritize clinicians' training needs. Clinician informaticists then provided 2-4 h of tailored training to groups of 1-5 clinicians. Compared with baseline scores [...]

Effective sign-outs involve verbal communication supported by written or electronic documentation. We investigated the clinical content overlap between sign-out documentation and face-to-face verbal sign-out communication.

This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they target in the human body. The focus of this research, though, is particularly centered on predicting the drug-ADE associations for a set of 8 common and high-risk ADEs.

To examine roles for summer internship programs in expanding pathways into biomedical informatics, based on 10 years of the Vanderbilt Department of Biomedical Informatics (DBMI) Summer Research Internship Program.

The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.

To compare physician productivity and billing before and after implementation of electronic charting in an academic emergency department (ED).

To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload.