Developing a Predictive Modeling Framework for Mental Health Outcomes

BACKGROUND AND QUESTION
Mental health disorders pose a significant challenge to public health and individual well-being. Early identification and prediction of mental health outcomes can lead to targeted interventions and improved treatment outcomes. This research proposal aims to develop a predictive modeling framework for mental health outcomes, schizophrenia, bipolar disorder, anxiety disorder, and major depressive disorder.

Schizophrenia is a severe and chronic mental health disorder characterized by a range of symptoms, including hallucinations, delusions, disorganized thinking, and impaired cognitive functions. It affects approximately 1% of the global population, making it one of the most prevalent psychiatric disorders worldwide. Schizophrenia has a significant impact on the lives of individuals affected and their families. The symptoms of schizophrenia can be distressing and debilitating, leading to impairments in social and occupational functioning. The disorder often results in a decreased quality of life, increased risk of unemployment, poverty, homelessness, and increased reliance on healthcare services. Individuals with schizophrenia are at an increased risk of developing comorbidities such as substance use disorders, depression, anxiety disorders, and physical health conditions. The presence of comorbidities further complicates the management of schizophrenia and requires integrated and multidisciplinary approaches.