AI Medical Compendium Topic:
Electronic Health Records

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Multi-task learning for Chinese clinical named entity recognition with external knowledge.

BMC medical informatics and decision making
BACKGROUND: Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-...

Machine learning and natural language processing to identify falls in electronic patient care records from ambulance attendances.

Informatics for health & social care
We derived machine learning models utilizing features generated by natural language processing (NLP) of free-text data from an ambulance services provider to identify fall cases. The data comprised samples of electronic patient care records care reco...

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies.

Journal of biomedical informatics
OBJECTIVE: Temporal electronic health records (EHRs) contain a wealth of information for secondary uses, such as clinical events prediction and chronic disease management. However, challenges exist for temporal data representation. We therefore sough...

A Novel Deep Learning-Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treat...

Safe medicine recommendation via star interactive enhanced-based transformer model.

Computers in biology and medicine
With the rapid development of electronic medical records (EMRs), most existing medicine recommendation systems based on EMRs explore knowledge from the diagnosis history to help doctors prescribe medication correctly. However, due to the limitations ...

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review.

Advances in wound care
Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big datasets over...

Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms.

Computational and mathematical methods in medicine
Cardiovascular disease (CVD) is one of the most common causes of death that kills approximately 17 million people annually. The main reasons behind CVD are myocardial infarction and the failure of the heart to pump blood normally. Doctors could diagn...

Leveraging medical context to recommend semantically similar terms for chart reviews.

BMC medical informatics and decision making
BACKGROUND: Information retrieval (IR) help clinicians answer questions posed to large collections of electronic medical records (EMRs), such as how best to identify a patient's cancer stage. One of the more promising approaches to IR for EMRs is to ...

Machine learning for classification of postoperative patient status using standardized medical data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analy...

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.

BMC medical informatics and decision making
BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to...