AIMC Topic: Patient Discharge Summaries

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Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.

JMIR medical informatics Jul 24, 2025
BACKGROUND: The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge not...
Patient Discharge Summaries Large Language Models Humans Machine Learning Natural Language Processing Patient Discharge Electronic Health Records Language
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A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Journal of biomedical informatics Jun 20, 2025
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...
Electronic Health Records Language Female Aged Patient Discharge Humans Male Patient Discharge Summaries Semantics Lung Neoplasms Large Language Models Natural Language Processing
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Automated generation of discharge summaries: leveraging large language models with clinical data.

Scientific reports May 12, 2025
This study explores the use of open-source large language models (LLMs) to automate generation of German discharge summaries from structured clinical data. The structured data used to produce AI-generated summaries were manually extracted from electr...
Patient Discharge Summaries Humans Language Large Language Models Electronic Health Records Patient Discharge Germany
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Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study.

JMIR medical informatics Apr 9, 2025
BACKGROUND: Valuable insights gathered by clinicians during their inquiries and documented in textual reports are often unavailable in the structured data recorded in electronic health records (EHRs).
Female Adult Male Middle Aged Algorithms Phenotype Electronic Health Records Data Mining Patient Discharge Summaries Cohort Studies Natural Language Processing Autoimmune Diseases Humans
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Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.

Drug safety Feb 21, 2025
INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of these LLMs in identifying relat...
Humans Machine Learning Large Language Models Patient Discharge Databases, Factual Drug-Related Side Effects and Adverse Reactions Patient Discharge Summaries Natural Language Processing Singapore Adverse Drug Reaction Reporting Systems
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FABLE: A Semi-Supervised Prescription Information Extraction System.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are crucial for patients' well-being and is often detailed in the narrative portions of EHRs. As a...
Natural Language Processing Supervised Machine Learning Datasets as Topic Patient Discharge Summaries Information Storage and Retrieval Drug Prescriptions Humans Electronic Health Records
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Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large...
Natural Language Processing Psychiatry Data Anonymization Humans Electronic Health Records Datasets as Topic Outpatients Machine Learning Diabetes Mellitus Patient Discharge Summaries Algorithms
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Evaluation of an automated knowledge-based textual summarization system for longitudinal clinical data, in the intensive care domain.

Artificial intelligence in medicine Sep 27, 2017
OBJECTIVES: To examine the feasibility of the automated creation of meaningful free-text summaries of longitudinal clinical records, using a new general methodology that we had recently developed; and to assess the potential benefits to the clinical ...
Continuity of Patient Care Medical Informatics Automation Patient Discharge Summaries Time Factors Retrospective Studies Health Status Electronic Health Records Humans Intensive Care Units Natural Language Processing Knowledge Bases Tertiary Care Centers Length of Stay Critical Care Feasibility Studies
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Prescription extraction using CRFs and word embeddings.

Journal of biomedical informatics Jul 4, 2017
In medical practices, doctors detail patients' care plan via discharge summaries written in the form of unstructured free texts, which among the others contain medication names and prescription information. Extracting prescriptions from discharge sum...
Natural Language Processing Information Storage and Retrieval Humans Machine Learning Patient Discharge Summaries Prescriptions
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Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.

Translational psychiatry Oct 18, 2016
The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable pre...
Kaplan-Meier Estimate Patient Discharge Summaries Massachusetts Narration Natural Language Processing Patient Readmission Humans Cohort Studies Electronic Health Records Time Factors Models, Statistical Male Female Risk Assessment Middle Aged Aged Depressive Disorder, Major Adult
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