Modeling disease severity in multiple sclerosis using electronic health records - Test2 - elhamkhani - TriplyDB

Modeling disease severity in multiple sclerosis using electronic health records

Modeling disease severity in multiple sclerosis using electronic health records

http://www.wikidata.org/entity/Q37297607

about

Extracting information from the text of electronic medical records to improve case detection: a systematic review Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources Quantifying a rare disease in administrative data: the example of calciphylaxis Identification of Nonresponse to Treatment Using Narrative Data in an Electronic Health Record Inflammatory Bowel Disease Cohort.Comorbidity in Adult Patients Hospitalized with Type 2 Diabetes in Northeast China: An Analysis of Hospital Discharge Data from 2002 to 2013 Neuroinflammation - using big data to inform clinical practice.Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts.Predictability Bounds of Electronic Health Records.Patient Electronic Health Records as a Means to Approach Genetic Research in Gastroenterology Common Genetic Variants Influence Circulating Vitamin D Levels in Inflammatory Bowel Diseases.The intelligent use and clinical benefits of electronic medical records in multiple sclerosis Development of phenotype algorithms using electronic medical records and incorporating natural language processing.Designing an Electronic Patient Management System for Multiple Sclerosis: Building a Next Generation Multiple Sclerosis Documentation System.Genes and Environment in Multiple Sclerosis project: A platform to investigate multiple sclerosis risk Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning Symptom severity classification with gradient tree boosting.Surrogate-assisted feature extraction for high-throughput phenotyping.Harnessing electronic medical records to advance research on multiple sclerosis.Enabling phenotypic big data with PheNorm.Clinical Information Extraction Applications: A Literature Review.The association of timing of disease-modifying drug initiation and relapse in patients with multiple sclerosis using electronic health records.Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.[Real-world evidence : Benefits and limitations in multiple sclerosis research].

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Modeling disease severity in multiple sclerosis using electronic health records

http://www.wikidata.org/entity/Q37297607

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2013 nî lūn-bûn

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2013年學術文章

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2013年學術文章

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2013年學術文章

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name

Modeling disease severity in multiple sclerosis using electronic health records

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Modeling disease severity in multiple sclerosis using electronic health records.

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type

label

Modeling disease severity in multiple sclerosis using electronic health records

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Modeling disease severity in multiple sclerosis using electronic health records.

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prefLabel

Modeling disease severity in multiple sclerosis using electronic health records

@en

Modeling disease severity in multiple sclerosis using electronic health records.

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Modeling disease severity in multiple sclerosis using electronic health records

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Andrew Cagan

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Ashwin N Ananthakrishnan

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Elizabeth Secor

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Elizabeth W Karlson

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Guergana K Savova

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Katherine P Liao

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Lori B Chibnik

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Pei J Chen

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Philip L De Jager

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Riley M Bove

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P2860

Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria

Mining electronic health records: towards better research applications and clinical care

Clinical characteristics of African Americans vs Caucasian Americans with multiple sclerosis

Multiple sclerosis

Validity of information on comorbidity derived rom ICD-9-CCM administrative data.

The measurement and clinical relevance of brain atrophy in multiple sclerosis.

Current state of information technologies for the clinical research enterprise across academic medical centers.

A putative Alzheimer's disease risk allele in PCK1 influences brain atrophy in multiple sclerosis.

Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

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Howard L. Weiner

Peter Szolovits

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electronic health record

multiple sclerosis

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