Phenome-Wide Association Study

[1]  Chen Lin,et al.  Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record , 2015, J. Am. Medical Informatics Assoc..

[2]  Gil Alterovitz,et al.  Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization , 2015, J. Am. Medical Informatics Assoc..

[3]  E. Nice,et al.  Interactomics: toward protein function and regulation , 2015, Expert review of proteomics.

[4]  Marylyn D. Ritchie,et al.  Detection of Pleiotropy through a Phenome-Wide Association Study (PheWAS) of Epidemiologic Data as Part of the Environmental Architecture for Genes Linked to Environment (EAGLE) Study , 2014, PLoS genetics.

[5]  Paul A. Harris,et al.  Secondary use of clinical data: The Vanderbilt approach , 2014, J. Biomed. Informatics.

[6]  Keith Marsolo,et al.  Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis , 2014, Front. Genet..

[7]  Olivier Bodenreider,et al.  Coverage of Rare Disease Names in Standard Terminologies and Implications for Patients, Providers, and Research , 2014, AMIA.

[8]  Gang Fu,et al.  Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data , 2014, Nucleic Acids Res..

[9]  Joshua C. Denny,et al.  R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment , 2014, Bioinform..

[10]  Suzette J. Bielinski,et al.  Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index , 2014, Front. Genet..

[11]  Zhan Ye,et al.  Phenome-wide association studies (PheWASs) for functional variants , 2014, European Journal of Human Genetics.

[12]  I. Kohane,et al.  Finding the missing link for big biomedical data. , 2014, JAMA.

[13]  Eric Farber-Eger,et al.  Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies , 2014, BioData Mining.

[14]  Li Li,et al.  dRiskKB: a large-scale disease-disease risk relationship knowledge base constructed from biomedical text , 2014, BMC Bioinformatics.

[15]  S. Hebbring.  The challenges, advantages and future of phenome-wide association studies , 2014, Immunology.

[16]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[17]  J. Pathak,et al.  Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[18]  W. Chung,et al.  Defining a comprehensive verotype using electronic health records for personalized medicine. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[19]  Dipak Kalra,et al.  Contribution of Clinical Archetypes, and the Challenges, towards Achieving Semantic Interoperability for EHRs , 2013, Healthcare informatics research.

[20]  Quan Ding,et al.  Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[21]  Patrice Degoulet,et al.  Phenome-Wide Association Studies on a Quantitative Trait: Application to TPMT Enzyme Activity and Thiopurine Therapy in Pharmacogenomics , 2013, PLoS Comput. Biol..

[22]  Damian Smedley,et al.  The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data , 2014, Nucleic Acids Res..

[23]  Stephen B. Johnson,et al.  A review of approaches to identifying patient phenotype cohorts using electronic health records , 2013, J. Am. Medical Informatics Assoc..

[24]  Marylyn D. Ritchie,et al.  Visualizing genomic information across chromosomes with PhenoGram , 2013, BioData Mining.

[25]  Anna Rumshisky,et al.  Temporal reasoning over clinical text: the state of the art , 2013, J. Am. Medical Informatics Assoc..

[26]  Gil Alterovitz,et al.  Brief communication: External phenome analysis enables a rational federated query strategy to detect changing rates of treatment-related complications associated with multiple myeloma , 2013, J. Am. Medical Informatics Assoc..

[27]  T. Lasko,et al.  Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data , 2013, PloS one.

[28]  Melissa A. Basford,et al.  The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future , 2013, Genetics in Medicine.

[29]  Sina Madani,et al.  Implementing an interface terminology for structured clinical documentation. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[30]  G. Hripcsak,et al.  Discovering medical conditions associated with periodontitis using linked electronic health records. , 2013, Journal of clinical periodontology.

[31]  Melissa A. Basford,et al.  Genome- and Phenome-Wide Analyses of Cardiac Conduction Identifies Markers of Arrhythmia Risk , 2013, Circulation.

[32]  Peter Szolovits,et al.  Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls. , 2013, Arthritis and rheumatism.

[33]  Steven J. Schrodi,et al.  A PheWAS approach in studying HLA-DRB1*1501 , 2013, Genes and Immunity.

[34]  T. Ideker,et al.  Genome-wide methylation profiles reveal quantitative views of human aging rates. , 2013, Molecular cell.

[35]  C. Bennett,et al.  Ascertainment of chronic diseases using population health data: a comparison of health administrative data and patient self-report , 2013, BMC Public Health.

[36]  Marylyn D. Ritchie,et al.  Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network , 2013, PLoS genetics.

[37]  Suzette J. Bielinski,et al.  Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank , 2012, Journal of Biomedical Semantics.

[38]  R. Nuño-Solínis,et al.  Monitoring the prevalence of chronic conditions: which data should we use? , 2012, BMC Health Services Research.

[39]  M. Ritchie,et al.  Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View , 2012, BioData Mining.

[40]  Hua Xu,et al.  Portability of an algorithm to identify rheumatoid arthritis in electronic health records , 2012, J. Am. Medical Informatics Assoc..

[41]  Katherine H. Huang,et al.  A framework for human microbiome research , 2012, Nature.

[42]  Lin Chen,et al.  Importance of multi-modal approaches to effectively identify cataract cases from electronic health records , 2012, J. Am. Medical Informatics Assoc..

[43]  Isaac S. Kohane,et al.  A translational engine at the national scale: informatics for integrating biology and the bedside , 2012, J. Am. Medical Informatics Assoc..

[44]  Suzette J. Bielinski,et al.  Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study , 2012, J. Am. Medical Informatics Assoc..

[45]  David W. Bates,et al.  A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record , 2011, J. Am. Medical Informatics Assoc..

[46]  Melissa A. Basford,et al.  Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. , 2011, American journal of human genetics.

[47]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[48]  Steven H. Brown,et al.  Automated identification of postoperative complications within an electronic medical record using natural language processing. , 2011, JAMA.

[49]  M. Fava,et al.  Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model , 2011, Psychological Medicine.

[50]  I. Kohane.  Using electronic health records to drive discovery in disease genomics , 2011, Nature Reviews Genetics.

[51]  T. H. Kyaw,et al.  Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.

[52]  Donna L. Hoyert,et al.  History of the statistical classification of diseases and causes of death , 2011 .

[53]  Hua Xu,et al.  Data from clinical notes: a perspective on the tension between structure and flexible documentation , 2011, J. Am. Medical Informatics Assoc..

[54]  Wendy A. Wolf,et al.  The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies , 2011, BMC Medical Genomics.

[55]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[56]  Christopher G. Chute,et al.  An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records , 2010, J. Biomed. Informatics.

[57]  R. Hegele,et al.  Phenomics: Expanding the Role of Clinical Evaluation in Genomic Studies , 2010, Journal of investigative medicine : the official publication of the American Federation for Clinical Research.

[58]  Marylyn D. Ritchie,et al.  PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations , 2010, Bioinform..

[59]  I. Kohane,et al.  Electronic medical records for discovery research in rheumatoid arthritis , 2010, Arthritis care & research.

[60]  Griffin M. Weber,et al.  Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2) , 2010, J. Am. Medical Informatics Assoc..

[61]  A. Meyer-Lindenberg,et al.  Intermediate or brainless phenotypes for psychiatric research? , 2009, Psychological Medicine.

[62]  J. Srigley,et al.  Standardized synoptic cancer pathology reporting: A population‐based approach , 2009, Journal of surgical oncology.

[63]  Albert-László Barabási,et al.  A Dynamic Network Approach for the Study of Human Phenotypes , 2009, PLoS Comput. Biol..

[64]  Franz Baader,et al.  SNOMED reaching its adolescence: Ontologists' and logicians' health check , 2009, Int. J. Medical Informatics.

[65]  D. Roden,et al.  Development of a Large‐Scale De‐Identified DNA Biobank to Enable Personalized Medicine , 2008, Clinical pharmacology and therapeutics.

[66]  John F. Hurdle,et al.  Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research , 2008, Yearbook of Medical Informatics.

[67]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[68]  A. Rzhetsky,et al.  Probing genetic overlap among complex human phenotypes , 2007, Proceedings of the National Academy of Sciences.

[69]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[70]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

[71]  J. Cimino.  Desiderata for Controlled Medical Vocabularies in the Twenty-First Century , 1998, Methods of Information in Medicine.

[72]  M. Cowen,et al.  Casemix adjustment of managed care claims data using the clinical classification for health policy research method. , 1998, Medical care.

[73]  A. Jablensky.  Methodological Issues in Psychiatric Classification , 1988, British Journal of Psychiatry.

[74]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[75]  C. Chute,et al.  A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects , 2013, Human Genetics.

[76]  Gil Alterovitz,et al.  Phenome-Based Analysis as a Means for Discovering Context-Dependent Clinical Reference Ranges , 2012, AMIA.

[77]  A. Barabasi,et al.  Network medicine : a network-based approach to human disease , 2010 .

[78]  Kazuhiko Ohe,et al.  Extraction of Adverse Drug Effects from Clinical Records , 2010, MedInfo.

[79]  Marylyn D. Ritchie,et al.  Finding Unique Filter Sets in PLATO: A Precursor to Efficient Interaction Analysis in GWAS Data , 2010, Pacific Symposium on Biocomputing.

[80]  Daniel M. Stein,et al.  Research paper: Quantifying clinical narrative redundancy in an electronic health record , 2010, J. Am. Medical Informatics Assoc..

[81]  Son Doan,et al.  Application of information technology: MedEx: a medication information extraction system for clinical narratives , 2010, J. Am. Medical Informatics Assoc..

[82]  J Ingenerf,et al.  Assessing Applicability of Ontological Principles to Different Types of Biomedical Vocabularies , 2009, Methods of Information in Medicine.

[83]  S. Trent Rosenbloom,et al.  Generating Complex Clinical Documents Using Structured Entry and Reporting , 2004, MedInfo.

[84]  Betsy L. Humphreys,et al.  Technical Milestone: The Unified Medical Language System: An Informatics Research Collaboration , 1998, J. Am. Medical Informatics Assoc..

[85]  Thomas Hobbes,et al.  LEVIATHAN Or the Matter Forme and Power of a Commonwealth Ecclesiasticall and Civil , 1946 .

[86]  J. Graunt,et al.  Natural and political observations made upon the bills of mortality , 1939 .