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

利用社交媒体数据和机器学习优化青少年精神分裂症的治疗模式

• 批准号: 9914128
• 负责人: Munmun De Choudhury
• 金额: $ 64万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9914128
• 关键词: Address; Adolescent and Young Adult; Adopted; Adoption; Affect; Aftercare; Behavior; Behavioral; Big Data; Biometry; Caring; Chronic; Circadian Rhythms; Clinical; Clinical Data; Clinical Psychology; Clinical Research; Clinical Trials; Cognition; Cognitive; Computational Technique; Computers; Computing Methodologies; Data; Data Collection; Deterioration; Development; Disorientation; Doctor of Philosophy; Early Diagnosis; Early Intervention; Emerging Technologies; Emotional; Evaluation; Evidence based treatment; Family; Feedback; Foundations; Goals; Health; Hospitalization; Improve Access; Individual; Intervention; Investigation; Knowledge; Lead; Learning; Linguistics; Machine Learning; Mental Depression; Mental Health; Mental disorders; Methods; Monitor; Moods; National Institute of Mental Health; Outcome; Patient-Focused Outcomes; Patients; Phase; Phenotype; Population; Privacy; Psychiatric therapeutic procedure; Psychiatry; Psychology; Psychotic Disorders; Relapse; Reporting; Research; Research Ethics; Research Methodology; Risk; Role; Sampling; Schizophrenia; Scientist; Severities; Site; Social Functioning; Social Psychology; Source; Stigmatization; Strategic Planning; Suicide; Surface; Symptoms; Techniques; Testing; Texas; Theoretical model; Time; Universities; Validation; Violence; Work; Youth; base; biological research; biomarker development; clinical decision support; clinical efficacy; clinical heterogeneity; cohort; computational basis; deep learning; digital; digital media; digital models; disability; disease classification; disorder later incidence prevention; evidence base; experience; family burden; first episode schizophrenia; high risk; improved; innovative technologies; intervention program; lens; machine learning method; medical complication; multidisciplinary; natural language; novel; novel strategies; peer; personalized intervention; prevent; prospective; psychosocial; psychotic symptoms; recruit; relapse patients; relapse prediction; relapse risk; response; social; social media; social observations; stem; support tools; treatment optimization; treatment response; treatment strategy; volunteer; willingness; young adult

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.

摘要 精神分裂症是一种慢性致残性疾病。虽然患者对治疗的应答率很高 在精神分裂症第一次发作后，fi的长期病程通常以频繁复发为特征。 失误，症状的持续性，以及持久的认知和功能的defiCits。尽管确定了 预防复发作为一项治疗目标，大约4/4的fiVe患者在fiFirstfiVe内复发。 多年的治疗。已知复发具有严重的心理社会、教育或职业影响。 年轻人--精神错乱的高危人群。然而，目前精神病学的识别能力指标 为了防止精神病症状升级，复发的可能性明显有限。挑战源于缺乏 关于预警迹象的全面信息的可用性，以及对fix时间点抽样的依赖 横断面数据以及患者或家庭报告的观察结果，受回忆偏差的影响，或基于临床- 医生寻求信息，这需要经常和及时的联系。目前的建议旨在解决这些问题 通过利用患者生成的和患者自愿的社交媒体数据，在早期精神病治疗方面存在差距， 以及开发和验证机器学习方法，用于“数字表型”和复发预测。我们的 拟议的工作基于这样的观察，即社交媒体网站已经成为 情感和语言表达--年轻人是社交媒体最频繁的用户之一。这项工作标志着- 积极推进研究议程和团队的广泛试点调查，他们a)证明了 个人的社交媒体数据可以作为理解和推断心理的强大“透镜” 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)参与审查新兴技术，如社交媒体在改善获取信息方面的作用 以及精神护理的提供。目标1将提供有理论依据和临床意义的方法 从青少年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 病人。然后在目标2，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fic个人化复发风险，并确定其前因。 最后，Aim 3将开发一个两方面的验证框架，以评估统计和临床效果fi的准确性和 社交媒体对精神病和复发的推断在fl使用临床结果和在 促进循证治疗。为了实现这些目标，该项目汇集了强大的多学科- 学科团队，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- TIES发起采用个性化、响应性和先发制人的循证治疗策略 精神错乱。这些知识将为未来启动旨在开发的大规模试验的研究奠定基础 减少复发严重程度的干预措施，或完全防止复发的发生。