A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations
This work addresses a domain-specific problem in biomedical informatics for classifying genetic mutations, but it is incremental as it builds on existing ensemble and multi-view methods.
The paper tackled the classification of clinically actionable genetic mutations from clinical literature text, achieving top performance in a competition with over 1,300 solutions, scoring 0.5506 and 0.6694 in logarithmic loss metrics.
This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel multi-view machine learning framework with ensemble classification models to solve the problem. During the Challenge, feature combinations derived from three views including document view, entity text view, and entity name view, which complements each other, are comprehensively explored. As the final solution, we submitted an ensemble of nine basic gradient boosting models which shows the best performance in the evaluation. The approach scores 0.5506 and 0.6694 in terms of logarithmic loss on a fixed split in stage-1 testing phase and 5-fold cross validation respectively, which also makes us ranked as a top-1 team out of more than 1,300 solutions in NIPS 2017 Competition Track IV.