![]() Stigma and discrimination continue to be a reality in the lives of people suffering from mental illness, particularly schizophrenia, and prove to be one of the greatest barriers to regaining a normal lifestyle and health. Our goal in this chapter was to discuss how these new techniques are likely to support essential clinical decisions in the forthcoming years.Clinical risk of stigma and discrimination of mental illnesses: Need for objective assessment and quantificationĪmresh Shrivastava 1, Yves Bureau 2, Nitika Rewari 3, Megan Johnston 4ġ Department of Psychiatry, University of Western Ontario, Lawson Health Research Institute, London Health Sciences Centre, London, Canada 2 University of Western Ontario, Lawson Health Research Institute, London Health Sciences Centre, London, Canada 3 Lawson Health Research Institute, London Health Sciences Centre, London, Canada 4 Department of Psychology, University of Toronto, Toronto, Ontario, CanadaĬlick here for correspondence address and Machine learning techniques can facilitate this process, as algorithms can identify what intervention each patient is most likely to respond to. Moreover, decision making regarding treatment selection is often a challenging process in BD. This predictive ability has the potential to change how we advise preventive measures in mental health environments. Also, this method can potentially predict patients’ prognosis and other relevant outcomes, such as suicide. This methodology has consistently shown evidence of its capacity to determine which high-risk subjects are more likely to convert to bipolar disorder (BD). Therefore machine learning techniques could be useful in the analysis of data associated with mental illnesses. These data-driven approaches analyze data without a preconceived hypothesis, allowing the free association of variables, which is ideal for assessing multifactorial disorders. ![]() ![]() 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. 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. ![]()
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