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Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for [...]

The Objective Structured Assessment of Debriefing (OSAD) is an evidence-based, 8-item tool that uses a behaviorally anchored rating scale in paper-based form to evaluate the quality of debriefing in medical education. The objective of this project was twofold: 1) to create an easy-to-use electronic format of the OSAD (eOSAD) in order to streamline data entry; and 2) to pilot its use on videoed debriefings.

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how 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.

To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules.

Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications.

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.