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To develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML).

Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making.

We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Toolkits are an important knowledge translation strategy for implementing digital health. We studied how toolkits for the implementation and evaluation of digital health were developed, tested, and reported.

Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population.

This study sought to describe gender representation in leadership and recognition within the U.S. biomedical informatics community.

To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

Inaccurate surgical preference cards (supply lists) are associated with higher direct costs, waste, and delays. Numerous preference card improvement projects have relied on institution-specific, manual approaches of limited reproducibility. We developed and tested an algorithm to facilitate the first automated, informatics-based, fully reproducible approach.

The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics.