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Machine learning could aid mental health diagnoses: Study - ET CIO

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Washington: In order to accurately identify patients with a mix of psychotic and depressive symptoms, researchers from the University of Birmingham recently developed a way of using machine learning to do so. The findings of the research were published in the journal'Schizophrenia Bulletin'. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a'primary' illness, but with secondary symptoms. Making an accurate diagnosis is a big challenge for clinicians and diagnoses often do not accurately reflect the complexity of individual experience or indeed neurobiology.


Machine learning could aid mental health diagnoses: Study

#artificialintelligence

Washington [US], February 28 (ANI): In order to accurately identify patients with a mix of psychotic and depressive symptoms, researchers from the University of Birmingham recently developed a way of using machine learning to do so. The findings of the research were published in the journal'Schizophrenia Bulletin'. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a'primary' illness, but with secondary symptoms. Making an accurate diagnosis is a big challenge for clinicians and diagnoses often do not accurately reflect the complexity of individual experience or indeed neurobiology.


Machine learning could aid mental health diagnoses: Study

#artificialintelligence

In order to accurately identify patients with a mix of psychotic and depressive symptoms, researchers from the University of Birmingham recently developed a way of using machine learning to do so. The findings of the research were published in the journal'Schizophrenia Bulletin'. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a'primary' illness, but with secondary symptoms. Making an accurate diagnosis is a big challenge for clinicians and diagnoses often do not accurately reflect the complexity of individual experience or indeed neurobiology.


ML technique could aid mental health diagnoses: Study

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Historically, this has meant that mental health clinicians give a diagnosis of a'primary' illness, but with secondary symptoms. "The majority of patients have comorbidities, so people with psychosis also have depressive symptoms and vice versa," said lead author Paris Alexandros Lalousis from the University of Birmingham in the UK. "That presents a big challenge for clinicians in terms of diagnosing and then delivering treatments that are designed for patients without co-morbidity. It's not that patients are misdiagnosed, but the current diagnostic categories we have do not accurately reflect the clinical and neurobiological reality," Lalousis added. For the study, published in the journal Schizophrenia Bulletin, the team explored the possibility of using ML to create highly accurate models of'pure' forms of both illnesses and to use these to investigate the diagnostic accuracy of a cohort of patients with mixed symptoms.


Study identifies brain-based dimensions of mental health disorders using machine learning

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A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients. A team at Penn Medicine led by Theodore D. Satterthwaite, MD, an assistant professor in the department of Psychiatry, mapped abnormalities in brain networks to four dimensions of psychopathology: mood, psychosis, fear, and disruptive externalizing behavior. The research is published in Nature Communications this week. Currently, psychiatry relies on patient reporting and physician observations alone for clinical decision making, while other branches of medicine have incorporated biomarkers to aid in diagnosis, determination of prognosis, and selection of treatment for patients. While previous studies using standard clinical diagnostic categories have found evidence for brain abnormalities, the high level of diversity within disorders and comorbidity between disorders has limited how this kind of research may lead to improvements in clinical care.