AIMC Topic: Electronic Health Records
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Fair and explainable Myocardial Infarction (MI) prediction: Novel strategies for feature selection and class imbalance correction.
Computers in biology and medicine
Nov 29, 2024
The rising incidences of myocardial infarction (MI), often affecting individuals without traditional risk factors, highlight the urgent need for improved early detection using personal health data. However, health surveys and electronic health record...
Developing a prediction model for cognitive impairment in older adults following critical illness.
BMC geriatrics
Nov 29, 2024
BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-I...
Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records.
BMC medical informatics and decision making
Nov 27, 2024
BACKGROUND: Rheumatoid Arthritis (RA) is a chronic inflammatory disease that is primarily diagnosed and managed by rheumatologists; however, it is often primary care providers who first encounter RA-related symptoms. This study developed and validate...
Large Language Models to Identify Advance Care Planning in Patients With Advanced Cancer.
Journal of pain and symptom management
Nov 24, 2024
CONTEXT: Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs) offers a novel approach to this task.
A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients.
Computers in biology and medicine
Nov 22, 2024
Sepsis, a life-threatening condition triggered by the body's response to infection, remains a significant global health challenge, annually affecting millions in the United States alone with substantial mortality and healthcare costs. Early predictio...
Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.
Journal of medical Internet research
Nov 22, 2024
BACKGROUND: Accurate hospital length of stay (LoS) prediction enables efficient resource management. Conventional LoS prediction models with limited covariates and nonstandardized data have limited reproducibility when applied to the general populati...
Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation.
JMIR medical informatics
Nov 21, 2024
BACKGROUND: Clinical named entity recognition (CNER) is a fundamental task in natural language processing used to extract named entities from electronic medical record texts. In recent years, with the continuous development of machine learning, deep ...
Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review.
Artificial intelligence in medicine
Nov 20, 2024
BACKGROUND: Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not promptly diagnosed and treated. Throughout time, medical research has sought to identify clinical facto...
Generating synthetic clinical text with local large language models to identify misdiagnosed limb fractures in radiology reports.
Artificial intelligence in medicine
Nov 20, 2024
Large language models (LLMs) demonstrate impressive capabilities in generating human-like content and have much potential to improve the performance and efficiency of healthcare. An important application of LLMs is to generate synthetic clinical repo...
A personalized periodontitis risk based on nonimage electronic dental records by machine learning.
Journal of dentistry
Nov 19, 2024
OBJECTIVE: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).
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