CUSTOM-SEQ: A prototype for oncology rapid learning in a comprehensive EHR environment

Journal of the American Medical Informatics Association

Volumn 23, Issue 4, 2016, Pages 692-700

  Warner, Jeremy L ^a,b   Wang, Lucy ^c   Pao, William ^a   Sosman, Jeffrey A ^a   Atreya, Ravi V ^b   Carney, Pam ^c   Levy, Mia A ^a,b,c  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^c VANDERBILT INGRAM CANCER CENTER   (United States)

Author keywords

Electronic health records; Genomics; Health information management; Information science; Neoplasms; Precision medicine

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR; ERLOTINIB; GUANINE NUCLEOTIDE BINDING PROTEIN; DNA; EPIDERMAL GROWTH FACTOR;

ADVANCED CANCER; ALGORITHM; ARTICLE; CANCER CHEMOTHERAPY; CANCER PATIENT; CANCER STAGING; CANCER SURVIVAL; CONTINUOUSLY UPDATING SYSTEM FOR TRACKING OUTCOME BY MUTATION TO SUPPORT EVIDENCE BASED QUERYING; ELECTRONIC HEALTH RECORD; FOLLOW UP; GENE MUTATION; GENOTYPE; HUMAN; LUNG CANCER; MACHINE LEARNING; MAJOR CLINICAL STUDY; MELANOMA; OVERALL SURVIVAL; SMOKING; BIOLOGY; COHORT ANALYSIS; GENETICS; INFORMATION RETRIEVAL; KAPLAN MEIER METHOD; LUNG TUMOR; MORTALITY; MUTATION; NEOPLASM; PERSONALIZED MEDICINE; PROPORTIONAL HAZARDS MODEL;

ALGORITHMS; COHORT STUDIES; COMPUTATIONAL BIOLOGY; DNA, NEOPLASM; ELECTRONIC HEALTH RECORDS; EPIDERMAL GROWTH FACTOR; FOLLOW-UP STUDIES; GENOTYPE; HUMANS; INFORMATION STORAGE AND RETRIEVAL; KAPLAN-MEIER ESTIMATE; LUNG NEOPLASMS; MUTATION; NEOPLASMS; PRECISION MEDICINE; PROPORTIONAL HAZARDS MODELS; TOBACCO SMOKING;

EID: 84981288112     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1093/jamia/ocw008     Document Type: Article

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