MARS: Assisting Human with Information Processing Tasks Using Machine Learning

Abstract

This article studies the problem of automated information processing from large volumes of unstructured, heterogeneous, and sometimes untrustworthy data sources. The main contribution is a novel framework called Machine Assisted Record Selection (MARS). Instead of today’s standard practice of relying on human experts to manually decide the order of records for processing, MARS learns the optimal record selection via an online learning algorithm. It further integrates algorithm-based record selection and processing with human-based error resolution to achieve a balanced task allocation between machine and human. Both fixed and adaptive MARS algorithms are proposed, leveraging different statistical knowledge about the existence, quality, and cost associated with the records. Experiments using semi-synthetic data that are generated from real-world patients record processing in the UK national cancer registry are carried out, which demonstrate significant (3 to 4 fold) performance gain over the fixed-order processing. MARS represents one of the few examples demonstrating that machine learning can assist humans with complex jobs by automating complex triaging tasks.