A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations

TL;DR

This paper introduces a multi-view ensemble machine learning framework that effectively classifies clinically actionable genetic mutations using text evidence, achieving top performance in a competitive challenge.

Contribution

The paper presents a novel multi-view ensemble approach combining document, entity text, and entity name features for mutation classification.

Findings

Achieved top ranking in NIPS 2017 Challenge with 0.5506 log loss.

Ensemble of nine gradient boosting models outperformed individual models.

Multi-view feature combination improved classification performance.

Abstract

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.