Ricerc@Sapienza

The development of innovative analytic tools for data-driven models is one of the most important issues in modern healthcare. The amount of data available either from a single patient or from a population of subjects could be difficult to understand and could slow down the diagnostic and therapeutic approach. Machine Learning (ML) are at the forefront of such a data-based revolution. However, descriptive or prescriptive models in healthcare need to be easily interpretable and assessable, but this is not the case of cutting edge ML algorithms such as Deep Neural Networks, Support Vector Machines or Random Forests and it poses a barrier to the adoption of these methods due to lack of explanations on the decisions. On the other hand, Decision Trees offer nice interpretability but lack the most important property in ML, which is generalization ability.
In this project, we propose to use Mixed Integer Optimization (MIO) to develop an optimal decision tree which encompasses hyperplane or even more complex splits that use multiple features for dichotomic branching at the nodes and allows to include further constraints on the characteristics of the final tree. The best values of the tree parameters are found using specialized exact algorithms.
The new ML model is used and compared with Deep Neural Networks, Support Vector Machines or Random Forests on two different problems in postural and rehabilitation medicine.
The project will comprise three connected threads:
1. design of new ML models and optimization algorithms;
2. Collection of postural data and definition of ML model for classification in healthy and not healthy;
3. Collection of data from medical records and definition of a ML model for the prediction of the rehabilitation outcome.