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

Showing 111 to 120 of 493 articles

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinica...

Foundation model-driven distributed learning for enhanced retinal age prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However,...

CACER: Clinical concept Annotations for Cancer Events and Relations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associated symptom burden...

Relation extraction using large language models: a case study on acupuncture point locations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama prese...

Generating colloquial radiology reports with large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide ...

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairme...

Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-s...

Representation of Social Determinants of Health terminology in medical subject headings: impact of added terms.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH).

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily id...