Machine learning for treatment optimization in subgroups of patients

In clinical trials comparing experimental and control treatment the effect of treatment often depends on the range of patient’s characteristics (biomarkers) such as clinical, anthropological, genetic, psychological, social characteristics and others. Personalized medicine aims at finding such dependencies to tailor treatment strategies to a patient. This paper presents an overview of the approaches to data analysis of clinical trials intended for identification of influential biomarkers and subgroups of patients, where experimental and control treatment differ significantly in efficiency.