Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.
about
Normalization of relative and incomplete temporal expressions in clinical narrativesAmbiguity and variability of database and software names in bioinformaticsEvaluating temporal relations in clinical text: 2012 i2b2 ChallengeEntity recognition from clinical texts via recurrent neural network.Adverse drug event detection in pediatric oncology and hematology patients: using medication triggers to identify patient harm in a specialized pediatric patient population.Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.Using local lexicalized rules to identify heart disease risk factors in clinical notesAn automatic system to identify heart disease risk factors in clinical texts over timeAutomating the generation of lexical patterns for processing free text in clinical documents.Background, Structure and Priorities of the 2013 Geneva Declaration on Person-centered Health Research.Electronic health records-driven phenotyping: challenges, recent advances, and perspectivesAutomatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record.Data Processing and Text Mining Technologies on Electronic Medical Records: A Review.Comparison of MetaMap and cTAKES for entity extraction in clinical notesPerson-Centered Psychiatric and Mental Health Research
P2860
Q28597787-65ABD4AD-878D-4EEE-8964-8732CB73C6DFQ28647795-79056F68-C8D8-4375-A49B-349C392C39FAQ28681390-AC675C69-365F-4412-95C3-CD298D98D437Q33896362-7054405F-2804-46E0-A54A-FD78CB7BC80CQ34090544-CBB59CBE-8962-4D75-ACB1-6CB0AE96C20CQ36397886-4A9235E4-3D20-49E4-849B-73B804C94432Q37153425-1FCC2985-C5CD-452A-96D6-56C9FA69B26FQ37165688-DF4A4574-1EB5-46FD-A3CC-3AACA333D491Q37179407-6E23C23B-3721-4FCF-93A2-45BE2CBDF487Q38493183-B0851FE1-69E0-4CB0-9750-7FA8333B731BQ42798335-372BE84A-A719-4D83-A2F1-DD5ACC967CFEQ45955993-D4BB1D09-345F-4739-92FA-5C91D52B7EE6Q55194590-01AD0365-662E-4E80-A469-6B1092C6E03DQ57157128-B86C0774-2F83-4856-87EB-5DB30828B89CQ58231029-5862B827-F33F-4439-B074-C6F7B0DF53D9
P2860
Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 20 April 2013
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Combining rules and machine le ...... ents from clinical narratives.
@en
Combining rules and machine le ...... ents from clinical narratives.
@nl
type
label
Combining rules and machine le ...... ents from clinical narratives.
@en
Combining rules and machine le ...... ents from clinical narratives.
@nl
prefLabel
Combining rules and machine le ...... ents from clinical narratives.
@en
Combining rules and machine le ...... ents from clinical narratives.
@nl
P2860
P50
P1476
Combining rules and machine le ...... ents from clinical narratives.
@en
P2093
John A Keane
Michele Filannino
P2860
P304
P356
10.1136/AMIAJNL-2013-001625
P577
2013-04-20T00:00:00Z