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Machine Learning Methods to Predict Outcomes of Pharmacological Treatment in Psychosis by Lorenzo Del Fabro; Elena Bondi; Francesca Serio; Eleonora Maggioni; Armando D’Agostino; Paolo Brambilla is a Neuroscience article available to read on EtoBox.

What is Machine Learning Methods to Predict Outcomes of Pharmacological Treatment in Psychosis about?

## Abstract In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small samp

Who reads Machine Learning Methods to Predict Outcomes of Pharmacological Treatment in Psychosis?

Author

Lorenzo Del Fabro; Elena Bondi; Francesca Serio; Eleonora Maggioni; Armando D’Agostino; Paolo Brambilla

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Neuroscience (Life Sciences)