  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.).
