AIMC Topic: Symptom Assessment
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ChatGPT-4 extraction of heart failure symptoms and signs from electronic health records.
Progress in cardiovascular diseases
Oct 21, 2024
BACKGROUND: Natural language processing (NLP) can facilitate research utilizing data from electronic health records (EHRs). Large language models can potentially improve NLP applications leveraging EHR notes. The objective of this study was to assess...
Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis.
Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
Jun 8, 2024
INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chronic lung disease. A complication of CF is a pulmonary exacerbation (PEx), which is often preceded by an increase in symptoms and a decline in lung funct...
Examining the differences between how doctors and artificial intelligence chatbots handle patient symptoms.
International journal of surgery (London, England)
Oct 1, 2023
Leveraging a Joint learning Model to Extract Mixture Symptom Mentions from Traditional Chinese Medicine Clinical Notes.
BioMed research international
Mar 8, 2022
This paper addresses the mixture symptom mention problem which appears in the structuring of Traditional Chinese Medicine (TCM). We accomplished this by disassembling mixture symptom mentions with entity relation extraction. Over 2,200 clinical notes...
Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.
Research in nursing & health
Oct 12, 2021
Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes an...
Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis.
Scientific reports
Sep 15, 2021
Decision making on the treatment of vestibular schwannoma (VS) is mainly based on the symptoms, tumor size, patient's preference, and experience of the medical team. Here we provide objective tools to support the decision process by answering two que...
A machine learning based exploration of COVID-19 mortality risk.
PloS one
Jul 2, 2021
Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning. This study aimed to develop and compare prognosis prediction machine learning models based on invas...
ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes.
Journal of biomedical informatics
Mar 25, 2021
OBJECTIVE: Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide A...
A natural language processing approach for identifying temporal disease onset information from mental healthcare text.
Scientific reports
Jan 12, 2021
Receiving timely and appropriate treatment is crucial for better health outcomes, and research on the contribution of specific variables is essential. In the mental health domain, an important research variable is the date of psychosis symptom onset,...
Nursing documentation of symptoms is associated with higher risk of emergency department visits and hospitalizations in homecare patients.
Nursing outlook
Dec 29, 2020
BACKGROUND: Nurses often document patient symptoms in narrative notes.
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