AIMC Topic: Electronic Health Records

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Exploring Prompt-Based Large Language Model (LLM) Approach for Medication Error-Related Named Entity Recognition in Medical Incident Reports.

Studies in health technology and informatics
Medication errors significantly challenge healthcare, necessitating innovative analytical methods. This study explored generative pre-trained language models (LLMs) for Named Entity Recognition (NER) in Japanese medical incident reports. We assessed ...

A Framework for Extracting, and Validating Named-Entities to Integrate Openehr Using the Example of Free Text Molecular Genetic Findings.

Studies in health technology and informatics
Processing and extracting information from unstructured texts written by physicians in Hospitals is still an open problem. There is no efficient solution that ensures the reliability of the extracted information without any human intervention. Many f...

Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis.

Studies in health technology and informatics
This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from ant...

Optimizing Nursing Records: Exploring the Impact of AI-Enhanced Documentation.

Studies in health technology and informatics
The standardization of medical records through structured templates has gained importance in improving the quality and safety of patient care. The results showed that missing rates ranged from 40.0% (fever reduction) to 43.9% (pain), while redundant ...

Generating Outpatient Progress Notes: A Comparison of Individualized and Generalized Models.

Studies in health technology and informatics
The increasing documentation workload in medical practice, particularly for clinical notes, has driven the development of AI-driven solutions. This study introduces an AI Doctor Assistant (DA) that generates drafts of outpatient progress notes. The D...

Evaluation of Synthetic Data Generation Methods for Medical Tabular Data: Representation of Distribution Tails.

Studies in health technology and informatics
Synthetic data generation by Artificial Intelligence (AI) and other means has the potential to share and analyze data while preserving privacy and maintaining statistical characteristics, and various methods have been developed. In medical datasets, ...

Performance of Open-Source Large Language Models to Extract Symptoms from Clinical Notes.

Studies in health technology and informatics
In this study, we examined how well the open-source foundational large language models (LLMs) can extract symptoms and signs (S&S), along with their corresponding ICD-10 codes, from clinical notes found in the public MTSamples dataset. The dataset co...

Human in the Loop: Embedding Medical Expert Input in Large Language Models for Clinical Applications.

Studies in health technology and informatics
The state-of-the-art performance of large language models (LLMs) in medical natural language (NLP) tasks, including medical query answering, summarization of clinical notes, and generation of medical reports has led to the development of a large numb...

Ambient Listening in Clinical Practice: Evaluating EPIC Signal Data Before and After Implementation and Its Impact on Physician Workload.

Studies in health technology and informatics
The widespread adoption of EHRs following the HITECH Act has increased the clinician documentation burden, contributing to burnout. Emerging technologies, such as ambient listening tools powered by generative AI, offer real-time, scribe-like document...

An Ensemble Approach Integrating Retrieval-Augmented Large Language Models and Boosting Algorithms for Enhanced Catatonia Phenotyping.

Studies in health technology and informatics
A critical first step in using large-scale data to study catatonia is the development of precise phenotyping algorithms that can identify instances of the condition. In this work, we present an ensemble approach that combines retrieval-augmented gene...