Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions
about
Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal DetectionTrends in biomedical informatics: automated topic analysis of JAMIA articlesAssessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) taskIdentifying plausible adverse drug reactions using knowledge extracted from the literatureComputational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworksToward enhanced pharmacovigilance using patient-generated data on the internetBig data and biomedical informatics: a challenging opportunity."Big data" and the electronic health record.A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions.Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS.Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data.Patient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events.Similarity-based modeling applied to signal detection in pharmacovigilance.Text mining for adverse drug events: the promise, challenges, and state of the art.Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives.Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement networkA time-indexed reference standard of adverse drug reactions.Efficiently mining Adverse Event Reporting System for multiple drug interactions.3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance.Portable automatic text classification for adverse drug reaction detection via multi-corpus training.Pharmacovigilance in oncology: evaluation of current practice and future perspectives.Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS)Social media and pharmacovigilance: A review of the opportunities and challenges.Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.Good Signal Detection Practices: Evidence from IMI PROTECTRevisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilanceA method for systematic discovery of adverse drug events from clinical notesA corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities.Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection.A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health recordsUsing Drug Similarities for Discovery of Possible Adverse Reactions.Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.Discovering adverse drug events combining spontaneous reports with electronic medical records: a case study of conventional DMARDs and biologics for rheumatoid arthritis.Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records.Prodromal signs and symptoms of serious infections with tocilizumab treatment for rheumatoid arthritis: Text mining of the Japanese postmarketing adverse event-reporting database.A Multiagent System for Integrated Detection of Pharmacovigilance Signals.A Personalized and Learning Approach for Identifying Drugs with Adverse EventsUse of internet search logs to evaluate potential drug adverse events.
P2860
Q27335410-D4234D12-F2E6-457E-B14B-5E18F3867000Q28596404-E348EB0D-C273-43EF-8171-64361062B3CBQ28602377-8441BAED-95D1-4772-8F23-03AD190C0CF5Q28650098-99227083-29E9-49C2-A98C-48CECDEBC58DQ28651547-51646C01-EC5B-46C6-B5A9-C229DB5907BCQ30796597-3E2E4FBC-EE00-437B-A4E1-19929110C9E9Q30826038-73862548-F845-41F9-8304-41410B1FE7B8Q30842802-F44D234A-432E-4D46-A3DF-0DED349603FDQ30979182-30C95E73-F5F3-4FAC-951B-5BE0798C24D6Q31159192-91D61B29-26BC-4233-8296-041A6412D3D3Q31172426-CD7F6892-D079-4ABD-A590-698D2D86E94FQ34154208-25EB99C8-2DE2-4029-AE30-F70EC9194837Q34413023-28354A7B-7ABB-411C-A6D3-7B81DB0EA880Q34442663-72535CFD-DD68-47BF-85BB-2D03AECDE1ECQ34881857-B49D6CEC-09D3-4E63-8E36-F283F7B2F5B8Q34996701-FF6CA813-78C0-44CD-8150-47A359A85FACQ35012194-0392BB5D-736F-480B-8C1E-515C12835C10Q35098986-2D6B1F45-60D7-47C2-BC19-BF6055838F65Q35153278-89DCCC45-496B-4448-B49E-766292858012Q35164490-486CD9F3-BDB5-499F-8E52-45C1611FABE3Q35183601-33D0E1F3-4D68-4293-BE88-D7233848B020Q35673776-5F630D52-F39A-4605-ADE6-3C2023E9BBA2Q36125558-92967BDB-52D0-43B5-A78A-B1A79591E382Q36320218-E8F9A8A6-47F3-4465-9AAF-8EF37312D9D6Q36413039-76B07161-D8B5-41BB-A612-E7BA41F8625DQ36914069-283F8402-9F25-47C8-826C-6D90F512301EQ36967198-C5A5729F-B332-412D-B884-0CF27BFEF7B9Q37040215-EFFAC390-1CBD-4B63-9035-31C7C9AE6C81Q37484790-A2E08F13-BFEB-41F5-97D9-FC00C44F97F8Q37531836-F5886C72-F866-4070-A9F4-182B572992EFQ37598934-EFA230F7-E357-43E6-8763-2A8A739647F3Q37676564-A3BBB11A-BD10-4096-8E04-2A6709DA7BAAQ38379019-C6C2A716-4B47-472B-9D1C-3D4D9806F055Q38621179-462BE1ED-AB30-448C-B6D8-796B4BF003B9Q38911464-43092308-BB6A-428D-9596-D5D609350EE3Q39021112-58617949-64A8-44E4-8088-0D5B83F3BE93Q40062836-6118FBBF-82DC-4B1F-97F5-00D21090A504Q40293236-5F546477-4B5B-4779-AD08-3989AC6B98BEQ42646906-58088433-3107-48C1-85AB-987ACC06264EQ43482153-13004066-8735-4D4B-96C9-0653A0DB4B42
P2860
Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Combing signals from spontaneo ...... tion of adverse drug reactions
@ast
Combing signals from spontaneo ...... tion of adverse drug reactions
@en
type
label
Combing signals from spontaneo ...... tion of adverse drug reactions
@ast
Combing signals from spontaneo ...... tion of adverse drug reactions
@en
prefLabel
Combing signals from spontaneo ...... tion of adverse drug reactions
@ast
Combing signals from spontaneo ...... tion of adverse drug reactions
@en
P2093
P2860
P1476
Combing signals from spontaneo ...... tion of adverse drug reactions
@en
P2093
Herbert S Chase
Hojjat Salmasian
Krystl Haerian
Nigam H Shah
Rave Harpaz
Santiago Vilar
William Dumouchel
P2860
P304
P356
10.1136/AMIAJNL-2012-000930
P577
2012-10-31T00:00:00Z