AIMC Topic: Electronic Health Records
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Applying stacking ensemble method to predict chronic kidney disease progression in Chinese population based on laboratory information system: a retrospective study.
PeerJ
Nov 1, 2024
BACKGROUND AND OBJECTIVE: Chronic kidney disease (CKD) is a major public health issue, and accurate prediction of the progression of kidney failure is critical for clinical decision-making and helps improve patient outcomes. As such, we aimed to deve...
Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.
European radiology
Oct 31, 2024
PURPOSE: Medical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available me...
Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.
Journal of the American Heart Association
Oct 25, 2024
BACKGROUND: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variabl...
Use of Deep Learning to Identify Peripheral Arterial Disease Cases From Narrative Clinical Notes.
The Journal of surgical research
Oct 24, 2024
INTRODUCTION: Peripheral arterial disease (PAD) is the leading cause of amputation in the United States. Despite affecting 8.5 million Americans and more than 200 million people globally, there are significant gaps in awareness by both patients and p...
A systematic review of networks for prognostic prediction of health outcomes and diagnostic prediction of health conditions within Electronic Health Records.
Artificial intelligence in medicine
Oct 23, 2024
BACKGROUND AND OBJECTIVE: Using graph theory, Electronic Health Records (EHRs) can be represented graphically to exploit the relational dependencies of the multiple information formats to improve Machine Learning (ML) prediction models. In this syste...
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...
Semiology Extraction and Machine Learning-Based Classification of Electronic Health Records for Patients With Epilepsy: Retrospective Analysis.
JMIR medical informatics
Oct 17, 2024
BACKGROUND: Obtaining and describing semiology efficiently and classifying seizure types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision support...
Enhancing Aortic Aneurysm Surveillance: Transformer Natural Language Processing for Flagging and Measuring in Radiology Reports.
Annals of vascular surgery
Oct 16, 2024
BACKGROUND: Incidental findings of aortic aneurysms (AAs) often go unreported, and established patients are frequently lost to follow-up. Natural language processing (NLP) offers a promising solution to address these issues. While rule-based NLP meth...
Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.
PeerJ
Oct 14, 2024
BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment cho...
Improving tabular data extraction in scanned laboratory reports using deep learning models.
Journal of biomedical informatics
Oct 10, 2024
OBJECTIVE: Medical laboratory testing is essential in healthcare, providing crucial data for diagnosis and treatment. Nevertheless, patients' lab testing results are often transferred via fax across healthcare organizations and are not immediately av...
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