Deep learning analysis of electrocardiography (ECG) may predict cardiovascular outcomes. We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead…
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Mental health apps are efficacious, yet they may pose risks in some. This review (CRD42024506486) examined adverse events (AEs) from mental health apps. We searched (May 2024) the Medline, PsycINFO, Web of Science, and…
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The US Department of Health and Human Services recently launched the National Healthcare Safety Dashboard that tracks patient and workforce harm. The goal is to monitor patient safety indicators so that improvement can result….
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This study demonstrates that adverse events (AEs) extracted using natural language processing (NLP) from clinical texts reflect the known frequencies of AEs associated with anticancer drugs. Using data from 44,502 cancer patients at a…
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The following is a guest article by Jay Nakashima, President at eHealth Exchange
An FDA project aims to make it as easy as possible for clinicians to report adverse drug events and share important clinical…
