JAMIA

January 2, 2024

Team uses COVID-19 to test automated acute disease profiling

An automated solution for creating phenotyping algorithms, PheNorm, worked well to identify symptomatic COVID-19 cases in electronic health records, suggesting that automation could speed high-throughput phenotyping of acute disease.

By Paul Govern

April 10, 2023

The problem with the problem list

Algorithms to infer missing problems and suggest that they be added to electronic health records improved problem list completeness, with benefits for clinical care, patient comprehension of health conditions and population health.

By Paul Govern

January 23, 2023

Machine learning predicts delirium

Using machine learning based on electronic health records of ICU patients predicted new-onset delirium with 82% sensitivity, Vanderbilt researchers found.

By Paul Govern

September 8, 2022

PheWAS reveals post-COVID-19 diagnoses

Using a high-throughput informatics technique and electronic health records, Vanderbilt researchers found that COVID-19 survivors had an increased risk for more than 40 new diagnoses.

By Bill Snyder

January 31, 2022

Impact of digital health interventions

Vanderbilt researchers test and recommend statistical approaches to study the association between engagement with digital health interventions and clinical outcomes.

By Leigh MacMillan

November 16, 2021

AI predicts 24-hour hospital discharge

Vanderbilt researchers used a machine learning algorithm and data from more than 26,000 hospital stays to predict who would and would not be discharged over the next 24 hours.