about
Text Mining the History of MedicineSHRINE: enabling nationally scalable multi-site disease studiesNatural language processing: an introductionIdentifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.Mining electronic health records: towards better research applications and clinical carePractice-based evidence: profiling the safety of cilostazol by text-mining of clinical notesNormalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2Virk: an active learning-based system for bootstrapping knowledge base development in the neurosciencesEvaluating temporal relations in clinical text: 2012 i2b2 ChallengeEvaluating the state of the art in coreference resolution for electronic medical recordsMining the pharmacogenomics literature--a survey of the state of the artAn evaluation of the UMLS in representing corpus derived clinical conceptsLinguistic scope-based and biological event-based speculation and negation annotations in the BioScope and Genia Event corporaCombining Structured and Free-text Data for Automatic Coding of Patient OutcomesPharmacovigilance Using Clinical NotesCommunity challenges in biomedical text mining over 10 years: success, failure and the future.Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.Description of a rule-based system for the i2b2 challenge in natural language processing for clinical dataAutomatic de-identification of textual documents in the electronic health record: a review of recent research.An interactive and user-centered computer system to predict physician's disease judgments in discharge summariesA Knowledge Intensive Approach to Mapping Clinical Narrative to LOINC.Comparing methods for identifying pancreatic cancer patients using electronic data sources.Automatic lymphoma classification with sentence subgraph mining from pathology reports.An electronic health record-enabled obesity database.Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)Extracting timing and status descriptors for colonoscopy testing from electronic medical records.Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.Extracting medication information from clinical text.Discovering peripheral arterial disease cases from radiology notes using natural language processing.Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicineImproved de-identification of physician notes through integrative modeling of both public and private medical text.2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text.Automatically detecting medications and the reason for their prescription in clinical narrative text documentsAutomatic computation of CHA2DS2-VASc score: information extraction from clinical texts for thromboembolism risk assessment.Annotating risk factors for heart disease in clinical narratives for diabetic patients.Using text mining techniques to extract phenotypic information from the PhenoCHF corpusThe role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.Challenges in clinical natural language processing for automated disorder normalization
P2860
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P2860
description
2009 nî lūn-bûn
@nan
2009 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Recognizing obesity and comorbidities in sparse data.
@ast
Recognizing obesity and comorbidities in sparse data.
@en
type
label
Recognizing obesity and comorbidities in sparse data.
@ast
Recognizing obesity and comorbidities in sparse data.
@en
prefLabel
Recognizing obesity and comorbidities in sparse data.
@ast
Recognizing obesity and comorbidities in sparse data.
@en
P2860
P356
P1476
Recognizing obesity and comorbidities in sparse data.
@en
P2860
P304
P356
10.1197/JAMIA.M3115
P50
P577
2009-04-23T00:00:00Z