Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.
about
Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational researchDescribing the relationship between cat bites and human depression using data from an electronic health recordEnhancing vaccine safety capacity globally: A lifecycle perspectiveUsing text-mining techniques in electronic patient records to identify ADRs from medicine useNatural language processing: an introductionMining electronic health records: towards better research applications and clinical careDrug safety surveillance using de-identified EMR and claims data: issues and challengesDeveloping a natural language processing application for measuring the quality of colonoscopy proceduresEnhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitisDetection of drug-drug interactions by modeling interaction profile fingerprintsIdentification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case RegisterTrends in biomedical informatics: automated topic analysis of JAMIA articlesAn integrated, ontology-driven approach to constructing observational databases for researchIdentifying plausible adverse drug reactions using knowledge extracted from the literatureStandardizing adverse drug event reporting dataA pipeline to extract drug-adverse event pairs from multiple data sourcesLessons learned from developing a drug evidence base to support pharmacovigilanceModeling temporal relationships in large scale clinical associationsMining the pharmacogenomics literature--a survey of the state of the artTrends in biomedical informatics: most cited topics from recent yearsMultiple chronic conditions and disabilities: implications for health services research and data demandsGeneralized enrichment analysis improves the detection of adverse drug events from the biomedical literatureA knowledge-poor approach to chemical-disease relation extractionIdentification of possible adverse drug reactions in clinical notes: The case of glucose-lowering medicinesPharmacovigilance Using Clinical NotesBirth month affects lifetime disease risk: a phenome-wide methodDetermining correspondences between high-frequency MedDRA concepts and SNOMED: a case studyFunctional evaluation of out-of-the-box text-mining tools for data-mining tasks.A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions.Characterizing environmental and phenotypic associations using information theory and electronic health records.Data-driven approach for creating synthetic electronic medical records.PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations.Selecting information in electronic health records for knowledge acquisitionPatient stratification and identification of adverse event correlations in the space of 1190 drug related adverse events.Automated evaluation of electronic discharge notes to assess quality of care for cardiovascular diseases using Medical Language Extraction and Encoding System (MedLEE).Similarity-based modeling applied to signal detection in pharmacovigilance.Text mining for adverse drug events: the promise, challenges, and state of the art.Chapter 13: Mining electronic health records in the genomics era.Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategiesMining adverse drug reactions from online healthcare forums using hidden Markov model
P2860
Q21146739-C5FBE11C-301A-414F-A632-AF70C9A739FDQ21559577-EE56CBCE-6417-442D-8575-6ADFE29ADB84Q26782451-95B7DCE4-FE0B-4C43-9455-E0D49B383CE3Q26864161-3E75CBC7-787D-4292-8450-FC73C53B47F5Q27000017-F51BFE60-DB9C-489A-93F8-7C44D54AEDB8Q27927409-B70A766E-AB68-40E2-B548-25905C0875B5Q28385191-57B83D5A-EF7D-4B27-9E70-01B1FABD234BQ28394901-9FA33949-E3C9-47FA-8F95-29F04279FFC1Q28481490-071BA8DB-3FDB-4F91-8513-C18F9F31AC47Q28487875-E1BBA61A-47C0-4114-B72A-DA8A63A4F363Q28547303-D9BA4047-6F7F-4767-90C5-115C9DEC2EF8Q28596404-1E9A3F42-C449-4B4D-A61A-AAC7A327D9B8Q28632523-97E3B60B-1B6C-4C57-B185-27C164C6DA95Q28650098-CF5CAFAB-3860-4251-BD32-D19EDF1C7073Q28654311-A784108E-A797-4AC7-A18E-E26A58EEA7D2Q28658495-B0B577FE-B2E5-497F-BF35-0CFB83EC8D72Q28660777-2862D6E6-456D-4738-BB74-20A82800E5EFQ28707471-787267F9-09EA-40BE-8DAA-2F2ABED60915Q28728833-86D18ED7-16CB-4E2C-95D5-1EB62E1C5739Q28741319-C8EDA11D-F216-45FA-9E38-AA9218603AF5Q28748662-F31BE48B-290E-4E97-A558-1CDF9345A817Q28829259-40BD1DBA-80F3-4F9C-94AA-26B10EA3C8EAQ28834085-529BC5E8-5CAD-4462-B6AC-41D0295377C4Q30002304-DEE47E18-0F1E-4E9C-9BC3-641B7FA1259FQ30058397-BF374B3B-F794-4D3D-9349-9DA0E9F5509FQ30375296-179B7D49-B0B3-446B-8D9B-8B668DE8C795Q30497421-BA449FBB-4EC7-42AB-ACE5-FF9C4ECC3A92Q30863601-B66FAC04-F402-47E8-B86D-2A1A250BE864Q30979182-DF242E9A-0F7E-4369-9742-913B1B8048EFQ33504365-4FABA05E-284E-489D-8C60-506E7B75DB83Q33718703-A0EDF2AD-6626-4609-A32A-49F9B4BD4728Q33808357-29B6E014-AE69-4750-B291-959033972DE8Q33987689-487100F5-E805-4E54-8601-3C7BC3B21348Q34154208-C023995D-0395-4FC7-BA44-C98F6DB9789EQ34372021-F0FCADEA-BDF1-496E-AFFC-597748B2FB27Q34413023-907BD086-57E5-4C63-B14A-9420ED054F3CQ34442663-30D507E9-0211-4C0A-81C0-18B34C076093Q34539676-6369FD7A-250D-40BE-9818-4147319ACC08Q34549552-5F298477-E6E4-414C-A82C-50569295E424Q34812232-DFF80C10-277B-4120-A3A1-9EC120D5CC0E
P2860
Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 04 March 2009
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Active computerized pharmacovi ...... records: a feasibility study.
@en
Active computerized pharmacovi ...... records: a feasibility study.
@nl
type
label
Active computerized pharmacovi ...... records: a feasibility study.
@en
Active computerized pharmacovi ...... records: a feasibility study.
@nl
prefLabel
Active computerized pharmacovi ...... records: a feasibility study.
@en
Active computerized pharmacovi ...... records: a feasibility study.
@nl
P2860
P50
P921
P356
P1476
Active computerized pharmacovi ...... records: a feasibility study.
@en
P2093
Xiaoyan Wang
P2860
P304
P356
10.1197/JAMIA.M3028
P577
2009-03-04T00:00:00Z