AIMC Topic: Electronic Health Records

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Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Journal of the American Heart Association
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
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
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
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
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
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
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
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...

Improving Clinical Decision Making With a Two-Stage Recommender System.

IEEE/ACM transactions on computational biology and bioinformatics
Clinical decision-making is complex and time-intensive. To help in this effort, clinical recommender systems (RS) have been designed to facilitate healthcare practitioners with personalized advice. However, designing an effective clinical RS poses ch...

Advances of artificial intelligence in predicting frailty using real-world data: A scoping review.

Ageing research reviews
BACKGROUND: Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language proc...