OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a...
INTRODUCTION: Long QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients.
Annals of clinical and laboratory science
Jul 1, 2021
OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opioids. Although subjective risk assessments are available to help classify subjects at risk for OUD, ...
BACKGROUND: Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form...
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 heal...
Oncological imaging is a subspecialty of medical imaging and focuses on the workup and the follow-up of cancer. Oncological imaging takes into account all the specificities of cancer diseases, which is a constantly evolving field, especially in the e...
PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images.
The Journal of clinical investigation
Apr 15, 2021
BACKGROUNDPatients with p16+ oropharyngeal squamous cell carcinoma (OPSCC) are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure for p16+ OPSCC. Pathologist-based visual assessment o...
BACKGROUND: Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.
IMPORTANCE: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to yo...
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