MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis

TL;DR

This paper introduces MD-MTL, an ensemble multi-task learning Python package designed for simultaneous disease score prediction and multi-level risk factor analysis, demonstrating advantages over single-task methods on healthcare datasets.

Contribution

The paper presents a novel ensemble MTL package for healthcare data analysis, enabling concurrent disease prediction and risk factor analysis across multiple patient subgroups.

Findings

MTL outperforms STL in multi-category healthcare data analysis

MD-MTL effectively predicts disease scores in different patient subgroups

The package facilitates comprehensive risk factor analysis at multiple levels

Abstract

While many machine learning methods have been used for medical prediction and risk factor analysis on healthcare data, most prior research has involved single-task learning (STL) methods. However, healthcare research often involves multiple related tasks. For instance, implementation of disease scores prediction and risk factor analysis in multiple subgroups of patients simultaneously and risk factor analysis at multi-levels synchronously. In this paper, we developed a new ensemble machine learning Python package based on multi-task learning (MTL), referred to as the Med-Multi-Task Learning (MD-MTL) package and applied it in predicting disease scores of patients, and in carrying out risk factor analysis on multiple subgroups of patients simultaneously. Our experimental results on two datasets demonstrate the utility of the MD-MTL package, and show the advantage of MTL (vs. STL), when analyzing data that is organized into different categories (tasks, which can be various age groups, different levels of disease severity, etc.).