AIMC Topic: Drug-Related Side Effects and Adverse Reactions

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Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

Drug safety Jan 1, 2019
INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable...
Humans Neural Networks, Computer Deep Learning Machine Learning Drug-Related Side Effects and Adverse Reactions Electronic Health Records
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Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0).

Drug safety Jan 1, 2019
INTRODUCTION: This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 challenge for extracting medication, indication, and adverse drug events (ADEs) fr...
Electronic Health Records Natural Language Processing Drug-Related Side Effects and Adverse Reactions Pattern Recognition, Automated Medication Systems Humans
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MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.

Drug safety Jan 1, 2019
INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs ...
Electronic Health Records Natural Language Processing Drug-Related Side Effects and Adverse Reactions Humans Machine Learning
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The Application of Machine Learning Techniques in Clinical Drug Therapy.

Current computer-aided drug design Jan 1, 2019
INTRODUCTION: The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adv...
Decision Trees Humans Drug-Related Side Effects and Adverse Reactions Machine Learning Drug Therapy Drug Discovery
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Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related ...
Support Vector Machine Social Media Natural Language Processing Humans Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Data Mining Pharmacovigilance
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A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they t...
Drug-Related Side Effects and Adverse Reactions Pharmacovigilance Datasets as Topic Adverse Drug Reaction Reporting Systems Computer Communication Networks Algorithms Models, Theoretical Drug Interactions Humans Machine Learning
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Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
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.
Supervised Machine Learning Drug-Related Side Effects and Adverse Reactions Data Mining Humans Natural Language Processing Databases, Bibliographic Pharmacovigilance Semantics Product Surveillance, Postmarketing
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Modeling polypharmacy side effects with graph convolutional networks.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Po...
Female Protein Interaction Maps Computational Biology Models, Biological Drug Interactions Drug-Related Side Effects and Adverse Reactions Data Visualization Humans Polypharmacy Neural Networks, Computer Software
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Detecting Chemotherapeutic Skin Adverse Reactions in Social Health Networks Using Deep Learning.

JAMA oncology Apr 1, 2018
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.
Social Networking Community Networks Antineoplastic Agents Deep Learning Data Mining Publications Humans Bias Information Storage and Retrieval Skin Diseases Product Surveillance, Postmarketing Drug-Related Side Effects and Adverse Reactions
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SuperDRUG2: a one stop resource for approved/marketed drugs.

Nucleic acids research Jan 4, 2018
Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete informa...
Marketing Drug-Related Side Effects and Adverse Reactions Knowledge Bases Internet Humans Pharmacokinetics Pharmaceutical Preparations User-Computer Interface Drug Approval Drug Interactions Databases, Pharmaceutical
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