Applying Machine Learning (ML) techniques on neuroanatomical Magnetic Resonance (MR) data, is becoming widespread for studying psychiatric disorders. However, such instruments require some precautions that, if not applied, may lead to inconsistent results that depend on the procedural choices made in the analysis, especially when the data under examination are extremely heterogeneous and many sources of bias are present. This is the case of studies on Autism Spectrum Disorder, in which the scarcity of data and the variability of this disease impose to examine data of subjects that differ both in the medical conditions and in the phenotypical characteristics. In this project two techniques that may be able to deal with these difficulties are proposed.
Novel Machine Learning Approaches in Multi-site Analysis for Autism Spectrum Disorders
FERRARI, ELISA;Maria Evelina Fantacci;
2018-01-01
Abstract
Applying Machine Learning (ML) techniques on neuroanatomical Magnetic Resonance (MR) data, is becoming widespread for studying psychiatric disorders. However, such instruments require some precautions that, if not applied, may lead to inconsistent results that depend on the procedural choices made in the analysis, especially when the data under examination are extremely heterogeneous and many sources of bias are present. This is the case of studies on Autism Spectrum Disorder, in which the scarcity of data and the variability of this disease impose to examine data of subjects that differ both in the medical conditions and in the phenotypical characteristics. In this project two techniques that may be able to deal with these difficulties are proposed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
