AI Medical Compendium Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 51 to 60 of 493 articles

Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Diagnosis codes documented in electronic health records (EHR) are often relied upon to clinically phenotype patients for biomedical research. However, these diagnoses can be incomplete and inaccurate, leading to false negatives when search...

Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously buil...

DiMB-RE: mining the scientific literature for diet-microbiome associations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To develop a corpus annotated for diet-microbiome associations from the biomedical literature and train natural language processing (NLP) models to identify these associations, thereby improving the understanding of their role in health a...

High-performance automated abstract screening with large language model ensembles.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: screening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract sc...

The value of simulation testing for the evaluation of ambient digital scribes: a case report.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The objective of this work is to demonstrate the value of simulation testing for rapidly evaluating artificial intelligence (AI) products.

Robust privacy amidst innovation with large language models through a critical assessment of the risks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study evaluates the integration of electronic health records (EHRs) and natural language processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language mo...

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...

Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of th...

How the National Library of Medicine should evolve in an era of artificial intelligence.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs).

Large language models are less effective at clinical prediction tasks than locally trained machine learning models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To determine the extent to which current large language models (LLMs) can serve as substitutes for traditional machine learning (ML) as clinical predictors using data from electronic health records (EHRs), we investigated various factors ...