Digital Phenotyping for Computational Models of Relapse Prediction in Early Course Psychosis

项目摘要 候选人请求支持为期四年的培训和研究计划，以更好地了解 基于智能手机的数字表型和计算方法如何预测复发和创造数字 早期精神病的症状表型和临床结果。 在拟议的培训计划中，候选人将建立在他以前的工程经验的基础上， 临床信息学和临床精神病学在贝丝以色列女执事中心执行一个多学科项目 医疗中心。他的培训计划包括：1)多变量纵向分析的统计方法 和预测性推理2)精神分裂症的神经精神病学评估3)纵向临床研究 以移动技术为重点的方法，以及4)负责任的研究行为。 即使有适当的护理，复发在早期精神病中也很常见，每一次发作都是 相关的较高的护理费用、较差的终生结局和疾病的慢性化。有必要 了解更多与个体患者复发相关的个人因素，以提高风险 预测，确保适当的早期干预，并支持协调的特殊护理服务 精神分裂症。这项研究提出，智能手机传感器(如GPS、加速计)、可穿戴设备等 收集生理信息的智能手表，以及基于智能手机的调查和认知测试，与 适当的统计方法，可以捕捉数字生物标记物，这里指的是数字表型，早期 病程精神病可以提供个性化的复发预测，并增加人群水平的风险因素。 这位候选人的研究计划寻求：1)提出早期疾病的数字表型和复发模型 也可以从受试者的个人智能手机上以负担得起且可扩展的方式捕获病程精神病 作为可穿戴传感器，以便自动收集症状、行为、认知和 生理学2)并评估数字表型和复发预测模型的准确性。 这项研究建议通过利用基于智能手机的数字表型来解决这一假设 方法，主要是通过在受试者自己的智能手机上运行北威应用程序来捕获纵向数据 关于受试者自然环境中的症状、行为、认知和生理。这些研究将 在患有早期精神病的受试者中进行为期3.5年的检查，范围为6至12个月。 这项研究的更广泛的目的是了解系统和过程，包括个人和 环境，这是导致早期精神病复发的原因。对计算的理解 复发的基础将提供更好的病因学信息，允许开发可能提供更好信息的疾病生物标记物 生物学研究的目标，为精神病个性化干预的发展提供信息，以及 帮助支持精神分裂症的早期干预。

Project Summary The candidate requests support for a four-year program of training and research to better understand how smartphone based digital phenotyping and computational methods can predict relapse and create digital phenotypes of symptoms and clinical outcomes in early course psychosis. In the proposed training plan, the candidate will build upon his previous experiences in engineering, clinical informatics, and clinical psychiatry to perform a multidisciplinary project at Beth Israel Deaconess Medical Center. His training plan includes training in: 1) statistical methods for multivariate longitudinal analysis and predictive inference 2) the neuropsychiatric assessment of schizophrenia 3) longitudinal clinical research methodology with a focus on mobile technologies, and 4) the responsible conduct of research. Even with appropriate care, relapse is common in early course psychosis and each episode is associated higher costs of care, poorer lifetime outcomes, and chronicity of the disease. There is a need to learn more about the personal factors associated with relapse for individual patients in order to improve risk predictions, ensure appropriate early interventions, and support coordinated specialty care services for schizophrenia. This study proposes that smartphones sensors eg (GPS, accelerometer), wearable devices like smartwatches collecting physiology, and smartphone based surveys and cognitive tests, when combined with appropriate statistical methods, can capture digital biomarkers, refereed to here as digital phenotypes, of early course psychosis that can offer personalized relapse prediction and augment population level risk factors. This candidate's research plan seeks to: 1) propose digital phenotypes and relapse models of early course psychosis captured in an affordable and scalable manner from subject's personal smartphones as well as a wearable sensor in order to automatically collect self-report of symptoms, behaviors, cognition, and physiology 2) and evaluate the accuracy of digital phenotypes and the relapse prediction models. This study proposes to address this hypothesis by utilizing smartphone based digital phenotyping methods, primarily through running the Beiwe app on subjects' own smartphones, to capture longitudinal data on symptoms, behaviors, cognition, and physiology across subjects' natural environments. These studies will be performed across 3.5 years in subjects with early course psychosis and range between 6 to 12 months. The broader aim of this research is to understand the systems and processes, both personal and environmental, which contribute to relapse in early course psychosis. An understanding of the computational basis of relapse will inform better nosology, allow development of biomarkers of illness that may offer better targets for biological research, inform development of personalized interventions for psychotic illnesses, and help support early interventions for schizophrenia.