Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural Networks

As the emerging field of predictive analytics in psychiatry generated and continues to generate massive interest overtime with its major promises to positively change and revolutionize clinical psychiatry, health care and medical professionals are greatly looking forward to its integration and application into psychiatry. However, by directly applying predictive analytics to the practice of psychiatry, this could cause detrimental damage to those that use predictive analytics through creating or worsening existing medical issues. In both cases, medical ethics issues arise, and need to be addressed. This paper will use literature to provide descriptions of selected stages in the treatment of mental disorders and phases in a predictive analytics project, approach mental disorder diagnoses using predictive models that rely on neural networks, analyze the complexities in clinical psychiatry, neural networks and predictive analytics, and conclude with emphasizing and elaborating on limitations and medical ethics issues of applying neural networks and predictive analytics to clinical psychiatry.