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- W4206670251 abstract "Machine learning-based studies, including data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk of developing a mental illness. This identification of relevant phenotypes is constructed through an analysis of each person’s unique biological profile, unlike some traditional statistical methods focused on group-level averages. These data-driven approaches analyze data without a preconceived hypothesis, allowing the free association of variables, which is ideal for assessing multifactorial disorders. Therefore machine learning techniques could be useful in the analysis of data associated with mental illnesses. This methodology has consistently shown evidence of its capacity to determine which high-risk subjects are more likely to convert to bipolar disorder (BD). Also, this method can potentially predict patients’ prognosis and other relevant outcomes, such as suicide. This predictive ability has the potential to change how we advise preventive measures in mental health environments. Moreover, decision making regarding treatment selection is often a challenging process in BD. Machine learning techniques can facilitate this process, as algorithms can identify what intervention each patient is most likely to respond to. Our goal in this chapter was to discuss how these new techniques are likely to support essential clinical decisions in the forthcoming years." @default.
- W4206670251 created "2022-01-25" @default.
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- W4206670251 date "2022-01-01" @default.
- W4206670251 modified "2023-09-25" @default.
- W4206670251 title "Precision psychiatry in bipolar disorder" @default.
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- W4206670251 doi "https://doi.org/10.1016/b978-0-12-821398-8.00001-1" @default.
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