TY - JOUR
T1 - Deep learning for the prediction of treatment response in depression
AU - Squarcina, Letizia
AU - Villa, Filippo Maria
AU - Nobile, Maria
AU - Grisan, Enrico
AU - Brambilla, Paolo
N1 - Funding Information:
MN and PB were partially supported by grants from the Italian Ministry of Health (RF-2016-02364582).
Funding Information:
PB was partially supported by grants from the Italian Ministry of Health (RF-2016-02364582).
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - Background: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. Methods: In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms “psychiatry”, “mood disorder”, “depression”, “treatment”, “deep learning”, “neural networks”. Only studies considering patients’ datasets are considered. Results: Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret. Limitations: The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods. Conclusions: Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of results.
AB - Background: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy on the basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity in the medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigate treatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and to integrate them with clinical, molecular or -omics biomarkers. Methods: In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographic search on PUBMED, Google Scholar and Web of Science using the terms “psychiatry”, “mood disorder”, “depression”, “treatment”, “deep learning”, “neural networks”. Only studies considering patients’ datasets are considered. Results: Eight studies met the inclusion criteria. Accuracies in prediction of response to therapy were considerably high in all studies, but results may be not easy to interpret. Limitations: The major limitation for the current studies is the small sample size, which constitutes an issue for machine learning methods. Conclusions: Deep learning shows promising results in terms of prediction of treatment response, often outperforming regression methods and reaching accuracies of around 80%. This could be of great help towards personalized medicine. However, more efforts are needed in terms of increasing datasets size and improved interpretability of results.
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U2 - 10.1016/j.jad.2020.11.104
DO - 10.1016/j.jad.2020.11.104
M3 - Review article
AN - SCOPUS:85096840304
JO - Journal of Affective Disorders
JF - Journal of Affective Disorders
SN - 0165-0327
ER -