Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC)

Massachusetts Veterans Epidemiology Research and Information Center Cooperative Studies Coordinating Center, VA Boston Healthcare System, Jamaica Plain, Massachusetts 02130, USA.
Journal of the American Medical Informatics Association (Impact Factor: 3.5). 07/2010; 17(4):375-82. DOI: 10.1136/jamia.2009.001412
Source: PubMed


Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application.

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Available from: Wildon R Farwell, Jan 02, 2014
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    • "NCI 1032 [60] SNOMED CT IHTSDO http://www.ihtsdo.org/snomed-ct/ SNOMEDCT 1353 [21] [26] [27] [30] [32] [45] Abbreviations: AMA, American Medical Association; ICH, International Conference on Harmonisation; IHTSDO, International Health Terminology Standards Development Organisation; NCI, National Cancer Institute; NLM, National Library of Medicine; WHO, World Health Organisation. Table 4 – Examples of cancer-specific ontologies. "
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    • "The next step of the analysis was performance of a supervised classification of retrieved, filtered affirmative 400-character document snippets as true or false positive using the Automated Retrieval Console developed by D'Avolio [12] [13]. This classifier utilizes the Mayo Ctakes (2) toolset for linguistic feature extraction, the UIMA pipeline architecture and the MALLET conditional random fields classifier. "
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    Proceedings of the 2014 47th Hawaii International Conference on System Sciences; 01/2014
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