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

Showing 161 to 170 of 493 articles

Streamlining social media information retrieval for public health research with deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical diction...

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

Development and external validation of deep learning clinical prediction models using variable-length time series data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected...

The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we cond...

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are tran...

Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the st...

Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow.

Multimodal learning for temporal relation extraction in clinical texts.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study focuses on refining temporal relation extraction within medical documents by introducing an innovative bimodal architecture. The overarching goal is to enhance our understanding of narrative processes in the medical domain, par...