Text mining for adverse drug events: the promise, challenges, and state of the art.
about
A curated and standardized adverse drug event resource to accelerate drug safety researchUtilizing social media data for pharmacovigilance: A reviewLeveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associationsMedication-indication knowledge bases: a systematic review and critical appraisalComputational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworksExtraction of chemical-induced diseases using prior knowledge and textual informationEvaluating Social Media Networks in Medicines Safety Surveillance: Two Case StudiesSymptom clusters in women with breast cancer: an analysis of data from social media and a research study.Exploiting heterogeneous publicly available data sources for drug safety surveillance: computational framework and case studies.Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS.Non-redundant association rules between diseases and medications: an automated method for knowledge base constructionLeveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies.Social media and pharmacovigilance: A review of the opportunities and challenges.Text mining for improved exposure assessmentNatural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.A method for systematic discovery of adverse drug events from clinical notesLeveraging the electronic health record to improve quality and safety in rheumatology.EliIE: An open-source information extraction system for clinical trial eligibility criteria.Accuracy of an automated knowledge base for identifying drug adverse reactions.A Multiagent System for Integrated Detection of Pharmacovigilance Signals.Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.Pharmacogenomics and big genomic data: from lab to clinic and back again.Pharmacovigilance Using Textual Data: The Need to Go Deeper and Wider into the Con(text).Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.What Can We Learn about Fall Risk Factors from EHR Nursing Notes? A Text Mining Study
P2860
Q24658583-47B5362A-AC66-4EAD-BE0D-B975389370A4Q27025237-7CC4A4DF-4C9D-4361-AB99-CFFC0E20459AQ27902320-1EAC3066-0E18-420E-8233-1D29BD9F8801Q28596416-64D6C012-E0A3-4432-AE22-88F3355677ACQ28651547-9B90CDB9-8BC4-4F8E-925E-F8C0165868CCQ28834680-157472DD-80D7-43A4-BFDF-D677DA4801B8Q30665207-CCC22705-B6C7-40B7-BE11-E919B457A22EQ31007428-C90DAAC0-3CA8-4C8B-8181-49733E9FC121Q31140921-A695B259-0DBB-4B58-9F9A-B0754CB4562AQ31159192-9C1D1459-7118-4E49-B469-254C5BA2A102Q35556146-9CBDFD83-20B0-4BC7-A52D-B9D3C0C715B5Q35765427-A4130D12-4181-4B1A-A156-655816351177Q36125558-5E6E1B7F-91EB-47AD-AF6F-6363FE4F49BFQ36296807-9C9E04B5-A718-4C52-8465-E06A53CB759BQ36413039-1A40E633-C323-422C-AB26-3D27FE169C3DQ37040215-EB308462-45B0-4EE7-90B7-856A9A3EEEB8Q38368489-F450488A-B0CE-4D6F-B56D-18FE10026DF0Q38853239-7C5BD6C9-16B0-4A93-9CE1-A5FC0CD941A6Q39086689-DC5DC7AF-52BB-4CAF-AEE6-0C4553E51AF9Q40293236-27B86DDD-61D4-4967-944E-D9A665365A01Q40955862-CC67DB06-59F0-4AF3-B559-58D3738945B9Q45943118-3ADFF993-3464-442F-A958-952F3803D11AQ45943393-F3ABC60C-9E96-4C2B-A294-CF3FA637F6FFQ47162508-3C46AE11-5993-4ECA-B96C-961EE2CBA726Q47295319-5D287BD4-78B8-4826-8444-B24242176FD8Q52596040-7D269F3F-CD47-4B56-8FA9-0ABC79CFEB0FQ53506590-C38E2AA4-6E55-4293-8A3D-3E0A4F8B7D57Q55455428-C456C3E7-B7D2-4326-B091-A7C7D9B01A80Q58710861-9E1DC073-102A-4D2F-8297-08787050BE43
P2860
Text mining for adverse drug events: the promise, challenges, and state of the art.
description
2014 nî lūn-bûn
@nan
2014 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年学术文章
@wuu
2014年学术文章
@zh-cn
2014年学术文章
@zh-hans
2014年学术文章
@zh-my
2014年学术文章
@zh-sg
2014年學術文章
@yue
name
Text mining for adverse drug events: the promise, challenges, and state of the art.
@ast
Text mining for adverse drug events: the promise, challenges, and state of the art.
@en
Text mining for adverse drug events: the promise, challenges, and state of the art.
@nl
type
label
Text mining for adverse drug events: the promise, challenges, and state of the art.
@ast
Text mining for adverse drug events: the promise, challenges, and state of the art.
@en
Text mining for adverse drug events: the promise, challenges, and state of the art.
@nl
prefLabel
Text mining for adverse drug events: the promise, challenges, and state of the art.
@ast
Text mining for adverse drug events: the promise, challenges, and state of the art.
@en
Text mining for adverse drug events: the promise, challenges, and state of the art.
@nl
P2093
P2860
P1433
P1476
Text mining for adverse drug events: the promise, challenges, and state of the art.
@en
P2093
David Odgers
Kenneth Jung
Nigam H Shah
Paea LePendu
Rave Harpaz
Sam Finlayson
Suzanne Tamang
P2860
P2888
P304
P356
10.1007/S40264-014-0218-Z
P577
2014-10-01T00:00:00Z
P5875
P6179
1018171280