AIMC Topic: Electronic Health Records

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A Comparison of Five Algorithmic Methods and Machine Learning Pattern Recognition for Artifact Detection in Electronic Records of Five Different Vital Signs: A Retrospective Analysis.

Anesthesiology
BACKGROUND: Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection a...

TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

Bioinformatics (Oxford, England)
MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data...

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Blood advances
Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medical record review and diagnosis code interpretation. Natural language processing (NLP) can automate ...

Comparing the quality of ChatGPT- and physician-generated responses to patients' dermatology questions in the electronic medical record.

Clinical and experimental dermatology
BACKGROUND: ChatGPT is a free artificial intelligence (AI)-based natural language processing tool that generates complex responses to inputs from users.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic ...

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. ...

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.

Translational vision science & technology
PURPOSE: To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pressure, use of ocular antihypertensive medications, and need for repeat surgery.