AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Product Surveillance, Postmarketing

Showing 11 to 20 of 20 articles

Clear Filters

Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.

AMIA ... Annual Symposium proceedings. AMIA Symposium
An important aspect of post-marketing drug surveillance involves identifying potential side-effects utilizing adverse drug event (ADE) reporting systems and/or Electronic Health Records. These data are noisy, necessitating identified drug/ADE associa...

Handling Temporality of Clinical Events for Drug Safety Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug event (ADE) detection allows for monitoring patients throughout their medical history. Machine learning methods have been shown to be efficient and effective ...

First-in-human robotic percutaneous coronary intervention for unprotected left main stenosis.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
The safety and feasibility of robotically-assisted percutaneous coronary intervention (PCI) for simple coronary lesions has been demonstrated. The CorPath robotic system (Corpath 200, Corindus, Waltham, MA) consists of a robotic arm mounted on the ca...

Detecting Chemotherapeutic Skin Adverse Reactions in Social Health Networks Using Deep Learning.

JAMA oncology
This study reports proof-of-principle early detection of chemotherapeutic-associated skin adverse drug reactions from social health networks using a deep learning–based signal generation pipeline to capture how patients describe cutaneous eruptions.

Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.

Ontology-based specification and generation of search queries for post-market surveillance.

Journal of biomedical semantics
BACKGROUND: The vigilant observation of medical devices during post-market surveillance (PMS) for identifying safety-relevant incidents is a non-trivial task. A wide range of sources has to be monitored in order to integrate all accessible data about...

Artificial Intelligence for Drug Toxicity and Safety.

Trends in pharmacological sciences
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and preve...

Learning to detect and understand drug discontinuation events from clinical narratives.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article...