AIMC Topic: Electronic Health Records

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Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.

PloS one
Capturing the dynamic changes in patients' internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on spe...

Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study.

JMIR medical informatics
BACKGROUND: Transportation insecurity is a known barrier to accessing eye care and is associated with poorer visual outcomes for patients. However, its mention is seldom captured in structured data fields in electronic health records, limiting effort...

Identifying Firearm Violence Exposure in Primary Care Clinical Notes: Protocol for Developing a National Language Processing Text Classifier.

JMIR research protocols
BACKGROUND: Structured data codes capture acute bodily injury from firearm violence but do not necessarily describe follow-up care from bodily injury and secondary exposure to firearm violence (eg, witnessing a shooting, being threatened by a firearm...

Quality and efficiency of integrating customised large language model-generated summaries versus physician-written summaries: a validation study.

BMJ open
OBJECTIVES: To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.

Machine learning predictions of unplanned readmissions using electronic medical records: Predictor importance across medical and surgical patient populations.

PloS one
Hospital readmissions prolong patient suffering and increase healthcare expenditures. While several studies have attempted to develop prediction models to reduce readmissions, most have demonstrated modest predictive accuracy. To improve upon prior a...

Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records.

PloS one
Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable ...

Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.

Circulation
BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their c...

A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.

Journal of biomedical semantics
BACKGROUND: For clinical care and research, knowledge graphs with patient data can be enriched by extracting parameters from a knowledge graph and then using them as inputs to compute new patient features with pure functions. Systematic and transpare...

Fibro predict a machine learning risk score for advanced liver fibrosis in the general population using Israeli electronic health records.

Scientific reports
Liver diseases, notably cirrhosis, pose a substantial global health challenge, resulting in millions of annual deaths. Existing diagnostic methods primarily target high-risk groups, leaving a significant portion of patients undiagnosed. This study ai...

Invisible Scribes: Can Nurses Trust Ambient AI for Clinical Documentation?

Journal of continuing education in nursing
Ambient artificial intelligence listening tools promise faster nursing documentation and improved patient engagement, yet they introduce risks of hallucinations, omission, and bias when nurses are excluded from the design and oversight process. Empow...