Work

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

Year: 2012

Type: article

Abstract: 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.

Source: PubMed Central

Authors: William Hsu, William Speier, Ricky K Taira

Institution: University of California, Los Angeles

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine

Awards: Medical Imaging Informatics Training Program, Toward Individually-tailored Medicine: Probabilistic Models of Cerebral Aneurysms, Data Structuring and Visualization System for Neuro-oncology