Leveraging Social Media Data and Machine Learning to Optimize Treatment Paradigms for Youth with Schizophrenia

摘要 精神分裂症是一种慢性致残性疾病。虽然患者对治疗的反应率很高， 在精神分裂症的第一次发作后，疾病的长期过程通常以频繁的复发为特征， 失误，症状持续存在，以及持久的认知和功能缺陷。尽管优先考虑 预防复发作为治疗目标，大约五分之四的患者在最初的五年内经历复发。 多年的治疗。众所周知，复发具有严重的社会心理、教育或职业影响， 年轻的成年人-精神病的高风险人群。然而，目前的精神病识别指标的能力， 复发，以防止精神病症状的升级是显着有限的。挑战源于缺乏 关于预警信号的全面信息的可用性，以及对固定时间点抽样的依赖 横截面数据以及患者或家庭报告的观察结果，这是受回忆偏见，或临床- 医生寻求信息，需要经常和及时联系。本提案旨在解决这些问题 通过利用患者生成和患者自愿的社交媒体数据， 以及开发和验证用于“数字表型”和复发预测的机器学习方法。我们 拟议的工作是建立在观察到社会媒体网站已成为突出的平台， 情感和语言表达-年轻人是社交媒体的最大用户。工作标志- icantly推进研究议程和广泛的试点调查的团队，谁a）已经证明 个人的社交媒体数据可以作为一个强大的“透镜”来理解和推断心理 健康状况、病程和复发的可能性，包括患有早期精神病的年轻人;以及 B）参与了研究新兴技术的作用，如社交媒体，在改善获得 和提供精神病治疗目标1将提供理论基础和临床意义的方法， 从年轻成人早期精神病的社交媒体数据中提取和建模数字表型和症状 患者然后在目标2中，我们将开发和评估机器学习方法，这些方法将利用提取的 社交媒体数字表型，以推断患者特定的个性化复发风险，并确定其前因。 最后，目标3将制定一个两方面的验证框架，以评估统计和临床有效性， 社交媒体衍生的精神病和复发的推断在确定临床结局和 促进循证治疗。为了实现这些目标，该项目汇集了一个强大的多学科， 初级团队，结合社交媒体分析，精神病学，心理学，自然语言分析， 机器学习、信息隐私和研究伦理。我们的新方法提供了前所未有的机会- 关系，以启动采用个性化，响应，和先发制人的循证策略，在治疗 精神病这些知识将为未来开展大规模试验的研究奠定基础， 减少复发的严重程度或完全预防复发的干预措施。

Abstract Schizophrenia constitutes a chronic and disabling illness. While patients show high rates of response to treatment after a first-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 deficits. Despite the prioritization of relapse prevention as a treatment goal, about four out of five patients experience a relapse within the first five 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 fixed 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-specific personalized risk of relapse, and identify its antecedents. Finally, Aim 3 will develop a two-faceted validation framework, to assess the statistical and clinical efficacy and utility of the social media derived inferences of psychosis and relapse in influencing 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.