Supporting Hypothesis Generation by Machine Learning in Smart Health

Abstract

The health care process involves a set of massive data that can be of different types and placed in different places. The digitization of information, if properly analysed, leads to an increase in the quality of processes and services in the Healthcare sector. In this context, Machine Learning (ML) is the key enabling technology to reduce the costs of health care extracting knowledge from data. The goal of ML is to improve a system without any continuous human intervention. Medical Decision Support Systems (MDSS) are an added value on the one hand by speeding up diagnosis and care processes, on the other hand reducing the time of medical staff or specialized learning. The paper proposes a new method to enable medical decision making improving collaboration between machine learning experts and clinicians in order to model individual treatments. The idea is to adapt machine learning models and decision support systems to address the challenging task of the decision of individual treatments in order to make more efficient decision-making and more effective therapies.