Using temporal patterns in medical records to discern adverse drug events from indications.
about
The Safety of Drug Therapy in ChildrenThe coming age of data-driven medicine: translational bioinformatics' next frontierPractice-based evidence: profiling the safety of cilostazol by text-mining of clinical notesAutomated detection of off-label drug useTowards a Computable Data Corpus of Temporal Correlations between Drug Administration and Lab Value ChangesDramatyping: a generic algorithm for detecting reasonable temporal correlations between drug administration and lab value alterationsStandardizing adverse drug event reporting dataPharmacovigilance Using Clinical NotesProfiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research.Translational bioinformatics embraces big data.Application of clinical text data for phenome-wide association studies (PheWASs).Annotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes.Analyzing patterns of drug use in clinical notes for patient safetyA method for systematic discovery of adverse drug events from clinical notesAutomated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical RecordsLearning signals of adverse drug-drug interactions from the unstructured text of electronic health recordsADEpedia 2.0: Integration of Normalized Adverse Drug Events (ADEs) Knowledge from the UMLS.Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records.Mining clinical text for signals of adverse drug-drug interactions.
P2860
Q28079478-AE6793C6-AD49-4A48-BB44-11EDC120B29DQ28386235-E132B4C4-6BAC-4599-9E62-75EC83BEA52FQ28533275-DC5394B2-7176-4B37-BE51-DA03698A29D7Q28539923-B00EDAAE-9391-45C5-8F12-65B5F9E26FAAQ28547437-546A80E1-77B2-44B3-B750-382823B71F83Q28601745-230E472B-5627-4018-AD26-FF3810D80C3BQ28654311-CF3451A6-0122-45D3-AC58-EBA453E6489AQ30058397-9C1AE695-F203-4B8F-B750-F9750075D96EQ34248642-0066B261-8F3F-46D6-9E50-06DA602F965BQ34378848-8A571456-D7C4-47E0-AC7D-F6311C3C4A17Q35556385-B082194D-EF74-475B-86A8-49005B3D3F75Q35914408-DA5F0A08-59B3-40E4-B1D8-5E50981433DBQ36082925-96241F29-F884-4BC5-B253-4C4130AAA230Q37040215-BB421FCF-C9C9-47C7-85B8-E8CA9E37ABD0Q37271400-1E7CADB0-1288-44E9-B78E-28B9650AB2ADQ37271421-4D78D413-6A70-4250-96F0-DEF314C91249Q37354400-BBED8EED-CCAE-44CB-8557-8C15FB1D5418Q37389419-4D2A1B0C-3F44-4C34-A039-B82CB7D1F7FBQ37598930-2EC6F675-A86B-4B54-A94C-31F4C28EC539
P2860
Using temporal patterns in medical records to discern adverse drug events from indications.
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
Using temporal patterns in med ...... drug events from indications.
@ast
Using temporal patterns in med ...... drug events from indications.
@en
type
label
Using temporal patterns in med ...... drug events from indications.
@ast
Using temporal patterns in med ...... drug events from indications.
@en
prefLabel
Using temporal patterns in med ...... drug events from indications.
@ast
Using temporal patterns in med ...... drug events from indications.
@en
P2093
P2860
P1433
P1476
Using temporal patterns in med ...... e drug events from indications
@en
P2093
Nigam H Shah
Paea Lependu
Srinivasan Iyer
P2860
P577
2012-03-19T00:00:00Z