- Home
- Publications
- Publication Search
- Publication Details
Title
Enabling phenotypic big data with PheNorm
Authors
Keywords
-
Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 25, Issue 1, Pages 54-60
Publisher
Oxford University Press (OUP)
Online
2017-09-14
DOI
10.1093/jamia/ocx111
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- EHR-based phenotyping: Bulk learning and evaluation
- (2017) Po-Hsiang Chiu et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods
- (2016) Rachel L. Richesson et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- Million Veteran Program: A mega-biobank to study genetic influences on health and disease
- (2016) John Michael Gaziano et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Learning statistical models of phenotypes using noisy labeled training data
- (2016) Vibhu Agarwal et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Electronic medical record phenotyping using the anchor and learn framework
- (2016) Yoni Halpern et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Large-scale identification of patients with cerebral aneurysms using natural language processing
- (2016) Victor M. Castro et al. NEUROLOGY
- Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources
- (2015) Sheng Yu et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Deep phenotyping: The details of disease
- (2015) Cathryn M. Delude NATURE
- Identification of subjects with polycystic ovary syndrome using electronic health records
- (2015) Victor Castro et al. Reproductive Biology and Endocrinology
- Development of phenotype algorithms using electronic medical records and incorporating natural language processing
- (2015) K. P. Liao et al. BMJ-British Medical Journal
- Development of phenotype algorithms using electronic medical records and incorporating natural language processing
- (2015) K. P. Liao et al. BMJ-British Medical Journal
- Methods to Develop an Electronic Medical Record Phenotype Algorithm to Compare the Risk of Coronary Artery Disease across 3 Chronic Disease Cohorts
- (2015) Katherine P. Liao et al. PLoS One
- Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records
- (2014) Jennifer A. Sinnott et al. HUMAN GENETICS
- Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing
- (2014) Sheng Yu et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Improving Case Definition of Crohnʼs Disease and Ulcerative Colitis in Electronic Medical Records Using Natural Language Processing
- (2013) Ashwin N. Ananthakrishnan et al. INFLAMMATORY BOWEL DISEASES
- Applying active learning to high-throughput phenotyping algorithms for electronic health records data
- (2013) Yukun Chen et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives
- (2013) Jyotishman Pathak et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Modeling Disease Severity in Multiple Sclerosis Using Electronic Health Records
- (2013) Zongqi Xia et al. PLoS One
- Pneumonia identification using statistical feature selection
- (2012) Cosmin Adrian Bejan et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Next-generation phenotyping of electronic health records
- (2012) G. Hripcsak et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies
- (2011) Catherine A McCarty et al. BMC Medical Genomics
- A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record
- (2011) Adam Wright et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
- (2011) Özlem Uzuner et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Using electronic health records to drive discovery in disease genomics
- (2011) Isaac S. Kohane NATURE REVIEWS GENETICS
- Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record
- (2010) Marylyn D. Ritchie et al. AMERICAN JOURNAL OF HUMAN GENETICS
- PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations
- (2010) J. C. Denny et al. BIOINFORMATICS
- Identification of Genomic Predictors of Atrioventricular Conduction
- (2010) Joshua C. Denny et al. CIRCULATION
- An automated technique for identifying associations between medications, laboratory results and problems
- (2010) Adam Wright et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications
- (2010) Guergana K Savova et al. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
- Phenomics: the next challenge
- (2010) David Houle et al. NATURE REVIEWS GENETICS
- A Genome-Wide Association Study of Red Blood Cell Traits Using the Electronic Medical Record
- (2010) Iftikhar J. Kullo et al. PLoS One
- Electronic medical records for discovery research in rheumatoid arthritis
- (2010) Katherine P. Liao et al. ARTHRITIS CARE & RESEARCH
- On the adaptive elastic-net with a diverging number of parameters
- (2009) Hui Zou et al. ANNALS OF STATISTICS
- Instrumenting the health care enterprise for discovery research in the genomic era
- (2009) S. Murphy et al. GENOME RESEARCH
- Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized Medicine
- (2008) DM Roden et al. CLINICAL PHARMACOLOGY & THERAPEUTICS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started