AI Medical Compendium Topic:
Electronic Health Records

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Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Journal of the American Heart Association
Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically describ...

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated.

Critical care clinics
Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers ...

An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.

International journal of radiation oncology, biology, physics
PURPOSE: Real-world evidence for radiation therapy (RT) is limited because it is often documented only in the clinical narrative. We developed a natural language processing system for automated extraction of detailed RT events from text to support cl...

Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records.

Medical & biological engineering & computing
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital ...

Review of Natural Language Processing in Pharmacology.

Pharmacological reviews
Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the...

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction.

IEEE transactions on cybernetics
Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, rea...

Integrating machine learning with linguistic features: A universal method for extraction and normalization of temporal expressions in Chinese texts.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: With the rapid development of information dissemination technology, the amount of events information contained in massive texts now far exceeds the intuitive cognition of humans, and it is hard to understand the progress of ...

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Scientific reports
Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery ...

A Natural Language Processing Model to Identify Confidential Content in Adolescent Clinical Notes.

Applied clinical informatics
BACKGROUND: The 21st Century Cures Act mandates the immediate, electronic release of health information to patients. However, in the case of adolescents, special consideration is required to ensure that confidentiality is maintained. The detection of...

Natural language processing in radiology: Clinical applications and future directions.

Clinical imaging
Natural language processing (NLP) is a wide range of techniques that allows computers to interact with human text. Applications of NLP in everyday life include language translation aids, chat bots, and text prediction. It has been increasingly utiliz...