AI Medical Compendium Topic:
Electronic Health Records

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Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

JAMA network open
IMPORTANCE: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in th...

Using natural language processing to classify social work interventions.

The American journal of managed care
OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs),...

Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.

Journal of biomedical informatics
OBJECTIVES: Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information from Electronic Health Records (EHRs). This is generally performed using advanced deep learning method...

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.

BMC medical informatics and decision making
BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are be...

A comprehensive study of mobility functioning information in clinical notes: Entity hierarchy, corpus annotation, and sequence labeling.

International journal of medical informatics
BACKGROUND: Secondary use of Electronic Health Records (EHRs) has mostly focused on health conditions (diseases and drugs). Function is an important health indicator in addition to morbidity and mortality. Nevertheless, function has been overlooked i...

Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing.

Journal of emergency nursing
INTRODUCTION: Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this retrospective study was to determine whether hi...

Use of Machine Learning to Develop a Risk-Stratification Tool for Emergency Department Patients With Acute Heart Failure.

Annals of emergency medicine
STUDY OBJECTIVE: We use variables from a recently derived acute heart failure risk-stratification rule (STRATIFY) as a basis to develop and optimize risk prediction using additional patient clinical data from electronic health records and machine-lea...