AIMC Topic: Electronic Health Records
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Investigating Older Adults' Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey.
Journal of medical Internet research
Dec 16, 2024
BACKGROUND: Given the public release of large language models, research is needed to explore whether older adults would be receptive to personalized medication advice given by artificial intelligence (AI) tools.
Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.
International journal of medical informatics
Dec 12, 2024
OBJECTIVE: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.
Assessing Large Language Models for Oncology Data Inference From Radiology Reports.
JCO clinical cancer informatics
Dec 11, 2024
PURPOSE: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.
Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.
JCO clinical cancer informatics
Dec 10, 2024
PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial fo...
Development and application of an intelligent pressure injury assessment system using AI image recognition.
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 9, 2024
BackgroundPressure injuries are a significant concern in clinical settings, requiring accurate assessment to prevent complications. Traditional assessment methods are often subjective and time-consuming.ObjectiveThis study aimed to develop and evalua...
Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.
Journal of vascular and interventional radiology : JVIR
Dec 9, 2024
To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone microwave ablation for lung tumors. In this single-center retrospective study, radio...
Neoplasm Recurrence, Local
Data Mining
Microwaves
Predictive Value of Tests
Natural Language Processing
Feasibility Studies
Ablation Techniques
Pneumothorax
Hemoptysis
Recurrence
Large Language Models
Hemothorax
Reproducibility of Results
Aged
Male
Lung Neoplasms
Treatment Outcome
Middle Aged
Time Factors
Retrospective Studies
Electronic Health Records
Female
Humans
LCDL: Classification of ICD codes based on disease label co-occurrence dependency and LongFormer with medical knowledge.
Artificial intelligence in medicine
Dec 7, 2024
Medical coding involves assigning codes to clinical free-text documents, specifically medical records that average over 3,000 markers, in order to track patient diagnoses and treatments. This is typically accomplished through manual assignments by he...
Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.
Clinical imaging
Dec 6, 2024
This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering additional perspectives on optimising ChatGPT-4 for Radiology applications. While the study highligh...
Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.
Advances in therapy
Dec 6, 2024
Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) res...
Early prediction of intensive care unit admission in emergency department patients using machine learning.
Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
Dec 5, 2024
BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.
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