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Drug-Related Side Effects and Adverse Reactions

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DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

BMC medical genomics
BACKGROUND: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mut...

Learning temporal weights of clinical events using variable importance.

BMC medical informatics and decision making
BACKGROUND: Longitudinal data sources, such as electronic health records (EHRs), are very valuable for monitoring adverse drug events (ADEs). However, ADEs are heavily under-reported in EHRs. Using machine learning algorithms to automatically detect ...

Ensembles of randomized trees using diverse distributed representations of clinical events.

BMC medical informatics and decision making
BACKGROUND: Learning deep representations of clinical events based on their distributions in electronic health records has been shown to allow for subsequent training of higher-performing predictive models compared to the use of shallow, count-based ...

An ensemble method for extracting adverse drug events from social media.

Artificial intelligence in medicine
OBJECTIVE: Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large dat...

Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events.

Journal of biomedical semantics
BACKGROUND: Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-saving chemotherapy treatment.

Predictive modeling of structured electronic health records for adverse drug event detection.

BMC medical informatics and decision making
BACKGROUND: The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. Th...

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 ...

Predicting drug side effects by multi-label learning and ensemble learning.

BMC bioinformatics
BACKGROUND: Predicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is ...

On the creation of a clinical gold standard corpus in Spanish: Mining adverse drug reactions.

Journal of biomedical informatics
The advances achieved in Natural Language Processing make it possible to automatically mine information from electronically created documents. Many Natural Language Processing methods that extract information from texts make use of annotated corpora,...

Exploring Spanish health social media for detecting drug effects.

BMC medical informatics and decision making
BACKGROUND: Adverse Drug reactions (ADR) cause a high number of deaths among hospitalized patients in developed countries. Major drug agencies have devoted a great interest in the early detection of ADRs due to their high incidence and increasing hea...