Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDIrelated adverse events. However, the implementation of DDI alerting system remains a...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE journal of biomedical and health informatics Ročník 24; číslo 1; s. 57 - 68
Hlavní autoři:	Liu, Ning, Chen, Cheng-Bang, Kumara, Soundar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adverse Drug Reaction Reporting Systems adverse event reports Algorithms classification clinical decision support Databases, Factual Decision Support Systems, Clinical drug-drug interactions Drug-Related Side Effects and Adverse Reactions - diagnosis Drug-Related Side Effects and Adverse Reactions - epidemiology Drug-Related Side Effects and Adverse Reactions - prevention & control Drugs Feature extraction Humans Information sources Learning algorithms Machine learning Machine learning algorithms Prediction algorithms Semi-supervised learning Semisupervised learning stacked autoencoder Supervised Machine Learning Support vector machines Workflow
ISSN:	2168-2194, 2168-2208, 2168-2208
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!