Generalizability of Risk Models for Treatment-Resistant Depression Across Three Health Systems.

Background: As multiple strategies have emerged for managing treatment-resistant major depressive disorder, efficient identification of individuals at elevated risk for this outcome earlier in their illness course remains essential.

Method: We extracted electronic health records data for all individuals with a diagnosis of major depressive disorder who received an index antidepressant prescription in the clinical networks of three geographically-distinct health systems - Mass General-Brigham (MGB), Vanderbilt University Medical Center (VUMC), and Geisinger Clinic (GC) - between April 1, 2004, and March 30, 2022. The primary outcome, treatment resistant depression, was defined as provision of electroconvulsive therapy, transcranial magnetic stimulation, vagus nerve stimulation, prescription of either ketamine or esketamine or monoamine oxidase inhibitors (MAOIs), or failed trials of more than two antidepressants.

Clinician Perspectives on a Predictive Model for Recommending Opioid Use Disorder Treatment.

Predictive models that have been made available as clinical decision support systems have not always been used. This qualitative study aimed to identify factors that might impact the uptake of a predictive model recommending either methadone or buprenorphine as medication for opioid use disorder (MOUD) in the inpatient setting. We conducted semi-structured interviews with clinicians who prescribe MOUD and performed a combined deductive and inductive content analysis using a socio-technical model.

Searching for Value Sensitive Design in Applied Health AI: A Narrative Review.

Objective: Recent advances in the implementation of healthcare artificial intelligence (AI) have drawn attention toward design methods to address the impacts on workflow. Lesser known than human-centered design, Value Sensitive Design (VSD) is an established framework integrating values into conceptual, technical, and empirical investigations of technology. We sought to study the current state of the literature intersecting elements of VSD with practical applications of healthcare AI.

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records with genetic data to understand which decisions may affect performance.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations.

Completeness and readability of GPT-4-generated multilingual discharge instructions in the pediatric emergency department.

Objectives: The aim of this study was to assess the completeness and readability of generative pre-trained transformer-4 (GPT-4)-generated discharge instructions at prespecified reading levels for common pediatric emergency room complaints.

Materials And Methods: The outputs for 6 discharge scenarios stratified by reading level (fifth or eighth grade) and language (English, Spanish) were generated fivefold using GPT-4. Specifically, 120 discharge instructions were produced and analyzed (6 scenarios: 60 in English, 60 in Spanish; 60 at a fifth-grade reading level, 60 at an eighth-grade reading level) and compared for completeness and readability (between language, between reading level, and stratified by group and reading level).

Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial.

Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III).

Characterization of Adult Patients with Autism Spectrum Disorder that Use Patient Portal.

Patients with autism spectrum disorder (ASD) access healthcare frequently, yet little is known about their interactions with patient portals. To describe adults with ASD using patient portal, we conducted regression analyses of visit history, demographics, co-occurring conditions and diagnoses, and patient portal use to determine factors most indicative of whether a patient 1) has sent at least one message (via patient or proxy) and 2) has at least one message sent on their behalf via a proxy account after they turned 18 years old. The 2,412-person cohort had 996 (41.

Divergent trends in accidental deaths since return from an Afghanistan/Iraq deployment among army soldiers.

Purpose: Accidental death is a leading cause of mortality among military members and Veterans; however, knowledge is limited regarding time-dependent risk following deployment and if there are differences by type of accidental death.

Methods: Longitudinal cohort study (N = 860,930) of soldiers returning from Afghanistan/Iraq deployments in fiscal years 2008-2014. Accidental deaths (i.

Scalable Incident Detection via Natural Language Processing and Probabilistic Language Models.

Post marketing safety surveillance depends in part on the ability to detect concerning clinical events at scale. Spontaneous reporting might be an effective component of safety surveillance, but it requires awareness and understanding among healthcare professionals to achieve its potential. Reliance on readily available structured data such as diagnostic codes risk under-coding and imprecision.

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance.

Sequential autoencoders for feature engineering and pretraining in major depressive disorder risk prediction.

Objectives: We evaluated autoencoders as a feature engineering and pretraining technique to improve major depressive disorder (MDD) prognostic risk prediction. Autoencoders can represent temporal feature relationships not identified by aggregate features. The predictive performance of autoencoders of multiple sequential structures was evaluated as feature engineering and pretraining strategies on an array of prediction tasks and compared to a restricted Boltzmann machine (RBM) and random forests as a benchmark.

Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study.

Background: Fatal drug overdose surveillance informs prevention but is often delayed because of autopsy report processing and death certificate coding. Autopsy reports contain narrative text describing scene evidence and medical history (similar to preliminary death scene investigation reports) and may serve as early data sources for identifying fatal drug overdoses. To facilitate timely fatal overdose reporting, natural language processing was applied to narrative texts from autopsies.