CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules

Journal Article · Fri Dec 21 00:00:00 EST 2018 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-018-2503-9· OSTI ID:1545570

Hengartner, Nicolas ^[1]; Cuellar, Leticia ^[1]; Wu, Xiao -Cheng ^[2]; Tourassi, Georgia  ^[3]; Qiu, John X. ^[3]; Christian, Blair  ^[3]; Bhattacharya, Tanmoy ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Louisiana State Univ., New Orleans, LA (United States)
3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information. This paper presents a mathematical framework to improve the precision of a classifier beyond that of the Bayes classifier by selectively classifying item that are most likely to be correct. This results in a triage rule that only classifies a subset of the item. We characterize the optimal triage rule and demonstrate its usefulness in the problem of classifying cancer site from electronic pathology reports to achieve a desired precision. From the mathematical formalism, we propose a heuristic estimate for triage rule based on post-processing the soft-max output from standard machine learning algorithms. We show, in test cases, that the triage rule significantly improve the classification accuracy.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1545570
• Journal Information: BMC Bioinformatics, Vol. 19, Issue S18; ISSN 1471-2105
• Publisher: BioMed Central
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
60 APPLIED LIFE SCIENCES
Machine learning
Classification