Schizophrenia constitutes a chronic and disabling illness. While patients show high rates of response to treatment after a ?rst-episode of schizophrenia, the long-term course of the illness is typically characterized by frequent re- lapses, persistence of symptoms, and enduring cognitive and functional de?cits. Despite the prioritization of relapse prevention as a treatment goal, about four out of ?ve patients experience a relapse within the ?rst ?ve years of treatment. Relapses are known to have serious psychosocial, educational, or vocational implications in young adults?a population at high risk of psychosis. However, current psychiatric ability to recognize indicators of relapse in order to prevent escalation of psychotic symptoms is markedly limited. Challenges stem from a lack of availability of comprehensive information about early warning signs, and reliance on ?xed time point sampling of cross-sectional data as well as patient or family reported observations, that is subject to recall bias, or on clin- ician sought information, that needs frequent and timely contact. The present proposal seeks to address these gaps in early psychosis treatment, by leveraging patient-generated and patient-volunteered social media data, and developing and validating machine learning approaches for ?digital phenotyping? and relapse prediction. Our proposed work is founded on the observation that social media sites have emerged as prominent platforms of emotional and linguistic expression?young adults are among the heaviest users of social media. The work signif- icantly advances the research agenda and extensive pilot investigations of the team, who a) have demonstrated that social media data of individuals can serve as a powerful ?lens? toward understanding and inferring mental health state, illness course, and likelihood of relapse, including among young adults with early psychosis; and b) have been involved in examining the role of emergent technologies, like social media, in improving access to and delivery of psychiatric care.
Aim 1 will provide theoretically-grounded and clinically meaningful methods for extracting and modeling digital phenotypes and symptoms from social media data of young adult early psychosis patients. Then in Aim 2, we will develop and evaluate machine learning methods that will utilize the extracted social media digital phenotypes to infer patient-speci?c personalized risk of relapse, and identify its antecedents. Finally, Aim 3 will develop a two-faceted validation framework, to assess the statistical and clinical ef?cacy and utility of the social media derived inferences of psychosis and relapse in in?uencing clinical outcomes and in facilitating evidence-based treatment. To accomplish these aims, the project brings together a strong multidisci- plinary team, combining expertise in social media analytics, psychiatry, psychology, natural language analysis, machine learning, information privacy, and research ethics. Our novel approach offers unprecedented opportuni- ties to initiate the adoption of personalized, responsive, and preemptive evidence-based strategies in treatment of psychosis. The knowledge will set the stage for future research on launching large-scale trials aimed to develop interventions that diminish the severity of relapses, or prevent their occurrence altogether.

Public Health Relevance

Timely monitoring of symptoms and preventing relapse after an initial psychotic episode are essential component of early intervention programs and have a critical impact on long term outcome in individuals with psychotic dis- orders. Employing patient-contributed social media data as a viable source of collateral information, this proposal provides a suite of robust, scalable, and ?eld evaluated machine learning methods to facilitate early and precise identi?cation of digital phenotypes, symptomatic exacerbation, and risk of psychotic relapse in early psychosis patients. The knowledge would provide the necessary opportunity to initiate personalized, adaptive, and proac- tive illness management strategies, inform better nosology, and assist the adoption of improved evidence-based care approaches to diminish the severity of relapses, or prevent their occurrence altogether.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Mental Health Services Research Committee (SERV)
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Azrin, Susan
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Georgia Institute of Technology
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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