predictive precision medicine towards the computational challenge

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level

Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder.

BACKGROUND: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning-based predictions of meaningful clinical improvement. METHODS: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7-15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. RESULTS: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. CONCLUSIONS: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures