Using data analytics and innovative research methodologies for the mapping of psychopathology
The structure of psychopathology determines how we identify people who need support services and how we can best help them. Currently, we identify those with psychopathological issues via assessments based on diagnostic manuals, such as the International Statistical Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM). However, there is a growing literature that has raised serious concerns about these two manuals. Some have suggested that such diagnostic manuals have misguided decades of mental health studies and may have contributed to dissatisfaction among service seekers and users relating to ineffective treatments and negative experiences with service providers. This doctoral dissertation explores possible alternative approaches to our understanding of the structure of psychopathology. It considers how these approaches could contribute to future classification, diagnostic and service delivery systems.
dsm 5, icd10 coding, diagnosis in mental health, machine learning, jaccad coefficient, k means clustering, network analysis, correlation, text-mining, patient narrative, diagnosis, diagnostic heterogenity, alternative diagnostics, psychopathology
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