AI Medical Compendium Topic:
Electronic Health Records

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Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy.

Urologic oncology
OBJECTIVE: To determine if the addition of electronic health record data enables better risk stratification and readmission prediction after radical cystectomy. Despite efforts to reduce their frequency and severity, complications and readmissions fo...

Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model.

Diabetes/metabolism research and reviews
AIMS: Identification, a priori, of those at high risk of progression from pre-diabetes to diabetes may enable targeted delivery of interventional programmes while avoiding the burden of prevention and treatment in those at low risk. We studied whethe...

Assessing stroke severity using electronic health record data: a machine learning approach.

BMC medical informatics and decision making
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...

Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation.

JAMA network open
IMPORTANCE: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its early detection could lead to significant improvements in outcomes through the appropriate prescription of anticoagulation medication. Although a variety of...

Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

Arthritis research & therapy
BACKGROUND: Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We devel...

A fusion framework to extract typical treatment patterns from electronic medical records.

Artificial intelligence in medicine
OBJECTIVE: Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify "right patient", "right drug", "right dose", "right route", and "r...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Journal of biomedical informatics
Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying...