Automated extraction of reported statistical analyses: towards a logical representation of clinical trial literature

Randomized controlled trials are an important source of evidence for guiding clinical decisions when treating a patient. However, given the large number of studies and their variability in quality, determining how to summarize reported results and formalize them as part of practice guidelines contin...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2012, Vol.2012, p.350-359
Main Authors: Hsu, William, Speier, William, Taira, Ricky K
Format: Article
Language:English
Subjects:
Automatic Data Processing
Carcinoma, Non-Small-Cell Lung
Evidence-Based Medicine
Humans
Information Storage and Retrieval - methods
Lung Neoplasms
Natural Language Processing
Randomized Controlled Trials as Topic - statistics & numerical data
Sensitivity and Specificity
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Summary:Randomized controlled trials are an important source of evidence for guiding clinical decisions when treating a patient. However, given the large number of studies and their variability in quality, determining how to summarize reported results and formalize them as part of practice guidelines continues to be a challenge. We have developed a set of information extraction and annotation tools to automate the identification of key information from papers related to the hypothesis, sample size, statistical test, confidence interval, significance level, and conclusions. We adapted the Automated Sequence Annotation Pipeline to map extracted phrases to relevant knowledge sources. We trained and tested our system on a corpus of 42 full-text articles related to chemotherapy of non-small cell lung cancer. On our test set of 7 papers, we obtained an overall precision of 86%, recall of 78%, and an F-score of 0.82 for classifying sentences. This work represents our efforts towards utilizing this information for quality assessment, meta-analysis, and modeling.
ISSN:1559-4076