Graph-based signal integration for high-throughput phenotyping

BMC Bioinformatics

Research

Devika Subramanian¹  Charles F Bearden²  Jorge R Herskovic²  Trevor Cohen²  Pamela A Bozzo-Silva³  Elmer V Bernstam⁴ 

[1] Department of Computer Science, Rice University, Houston, TX, USA;School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA;School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA;Escuela de Medicina, Universidad de Los Andes, Santiago, Chile;School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA;Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA;

关键词: Breast Cancer;    Tonsillitis;    External Knowledge;    Clinical Note;    Unify Medical Language System;   

DOI  :  10.1186/1471-2105-13-S13-S2

来源: Springer

【 摘 要 】

BackgroundElectronic Health Records aggregated in Clinical Data Warehouses (CDWs) promise to revolutionize Comparative Effectiveness Research and suggest new avenues of research. However, the effectiveness of CDWs is diminished by the lack of properly labeled data. We present a novel approach that integrates knowledge from the CDW, the biomedical literature, and the Unified Medical Language System (UMLS) to perform high-throughput phenotyping. In this paper, we automatically construct a graphical knowledge model and then use it to phenotype breast cancer patients. We compare the performance of this approach to using MetaMap when labeling records.ResultsMetaMap's overall accuracy at identifying breast cancer patients was 51.1% (n=428); recall=85.4%, precision=26.2%, and F1=40.1%. Our unsupervised graph-based high-throughput phenotyping had accuracy of 84.1%; recall=46.3%, precision=61.2%, and F1=52.8%.ConclusionsWe conclude that our approach is a promising alternative for unsupervised high-throughput phenotyping.

【 授权许可】

CC BY   
© Herskovic et al; licensee BioMed Central Ltd. 2012