AIMC Topic: Electronic Health Records

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Leveraging Data Pipeline and LLM to Advance Patient Safety Event Studies.

Studies in health technology and informatics
Research utilizing the open-access MAUDE database frequently reveals unclear methodologies for extracting and processing medical device report (MDR) data, reducing reproducibility and consistency. By harnessing the OpenFDA API and our MAUDE extract-t...

An Integrated AI Cloud Sharing Framework for Predictive AI and Generative AI in Healthcare.

Studies in health technology and informatics
In 2019, Chi Mei Hospital built a private cloud AI service framework, incorporating HIS interface web service, data retrieval web service, and AI web service. Numerous Predictive AI (PAI) applications were deployed successfully. By 2023, the hospital...

Subgroup Discovery to Identify Determinants of Influence on CDSS Medication Alert Handling: A Feasibility Study.

Studies in health technology and informatics
Clinical decision support systems (CDSSs) are designed to enhance patient safety by providing alerts to prescribers about potential medication issues. However, a significant proportion of these alerts are ignored, which can compromise patient safety....

On the Harmonisation of Time Series Data for the Optimisation of Machine Learning Using the Example of Rejection Prediction After Kidney Transplantation.

Studies in health technology and informatics
A significant risk following a kidney transplantation is graft loss. The Screen Reject Project has developed a Clinical Data Warehouse (CDWH) as a foundation for a clinical decision support system designed to improve the diagnosis of graft rejections...

Clustering Event Trajectories with Machine Learning: An Approach for Electronic Healthcare Records.

Studies in health technology and informatics
Multimorbidity is increasingly prevalent as the population ages and individuals with multiple long-term conditions (MLTCs) live longer. Often each condition is treated by a separate clinician, which can lead to harmful drug-drug and drug-disease inte...

Extracting Multifaceted Characteristics of Patients With Chronic Disease Comorbidity: Framework Development Using Large Language Models.

JMIR medical informatics
BACKGROUND: Research on chronic multimorbidity has increasingly become a focal point with the aging of the population. Many studies in this area require detailed patient characteristic information. However, the current methods for extracting such inf...

A Deep Learning-Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records.

JMIR cancer
BACKGROUND: Progression-free survival (PFS) is a crucial endpoint in cancer drug research. Clinician-confirmed cancer progression, namely real-world PFS (rwPFS) in unstructured text (ie, clinical notes), serves as a reasonable surrogate for real-worl...

Scientific Evidence for Clinical Text Summarization Using Large Language Models: Scoping Review.

Journal of medical Internet research
BACKGROUND: Information overload in electronic health records requires effective solutions to alleviate clinicians' administrative tasks. Automatically summarizing clinical text has gained significant attention with the rise of large language models....

Breaking Digital Health Barriers Through a Large Language Model-Based Tool for Automated Observational Medical Outcomes Partnership Mapping: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The integration of diverse clinical data sources requires standardization through models such as Observational Medical Outcomes Partnership (OMOP). However, mapping data elements to OMOP concepts demands significant technical expertise an...

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

Nursing research
BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used ...