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

基本信息

批准号：
10369003
负责人：
Munmun De Choudhury
金额：
$ 62.99万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-15 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10369003
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 Psychoses 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 early psychosis 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 psychosis risk 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

项目摘要

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.

抽象的精神分裂症是一种慢性致残性疾病，而患者对治疗的反应率很高。精神分裂症首次发作后，该病的长期病程通常以频繁复发为特征。尽管优先考虑了失误、症状持续存在以及持久的认知和功能缺陷。将预防复发作为治疗目标，大约五分之四的患者在前五年内会出现复发众所周知，多年的治疗复发会对心理、教育或职业产生严重影响。年轻人——精神病高危人群。然而，目前精神病学识别指标的能力。为防止精神病症状升级而采取的措施明显有限。挑战源于缺乏。有关预警信号的综合信息的可用性以及对固定时间点采样的依赖横截面数据以及患者或家属报告的观察结果，这些数据可能存在回忆偏差，或与临床有关 ician 寻求需要经常和及时联系的信息，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来弥补早期精神病治疗的差距，开发和验证“数字表型”和复发预测的机器学习方法。拟议的工作是基于以下观察：社交媒体网站已成为重要的平台情感和语言表达——年轻人是社交媒体最频繁的用户之一。巧妙地推进了研究议程和团队的广泛试点调查，他们a）已经证明个人的社交媒体数据可以作为理解和推断心理的强大“镜头” 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) 参与研究社交媒体等新兴技术在改善获取信息的机会方面的作用目标 1 将为患者提供有理论依据和临床意义的方法。从年轻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然后在目标 2 中，我们将开发和评估利用提取的机器学习方法。社交媒体数字表型来推断患者特定的个性化复发风险，并确定其前因。最后，Aim 3将开发一个双向验证框架，以评估统计和临床效率以及社交媒体对精神病和复发的推论在影响临床结果和为了促进循证治疗，该项目汇集了强大的多学科团队。核心团队，结合了社交媒体分析、精神病学、心理学、自然语言分析、我们的新颖方法提供了前所未有的机会。联系以启动采用个性化、响应性和先发性的循证策略来治疗这些知识将为未来开展旨在开发精神病的大规模试验奠定基础。可以减轻复发严重程度或完全预防复发的干扰。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Social Media Discussions Predict Mental Health Consultations on College Campuses.

社交媒体讨论预示着大学校园的心理健康咨询。

DOI：
发表时间：
2022-01-07
期刊：
影响因子：
4.6
作者：
Saha, Koustuv;Yousuf, Asra;Boyd, Ryan L;Pennebaker, James W;De Choudhury, Munmun
通讯作者：
De Choudhury, Munmun

A Social Media Study on Demographic Differences in Perceived Job Satisfaction.

关于工作满意度感知的人口统计差异的社交媒体研究。

DOI：
发表时间：
2021-04
期刊：
影响因子：
0
作者：
Saha, Koustuv;Yousuf, Asra;Hickman, Louis;Gupta, Pranshu;Tay, Louis;DE Choudhury, Munmun
通讯作者：
DE Choudhury, Munmun

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.

用于捕捉饮食时刻并触发生态瞬时评估以获得进一步背景的实时饮食检测系统：系统开发和验证研究。

DOI：
发表时间：
2020-12-18
期刊：
影响因子：
5
作者：
Bin Morshed, Mehrab;Kulkarni, Samruddhi Shreeram;Li, Richard;Saha, Koustuv;Roper, Leah Galante;Nachman, Lama;Lu, Hong;Mirabella, Lucia;Srivastava, Sanjeev;De Choudhury, Munmun;de Barbaro, Kaya;Ploetz, Thomas;Abowd, Gregory D
通讯作者：
Abowd, Gregory D

Examining the impact of sharing COVID-19 misinformation online on mental health.

研究在线分享 COVID-19 错误信息对心理健康的影响。