Automated Mental Health Referral System

这项提案解决了大学生获得治疗的一大障碍。 患有精神障碍的年轻人。许多患有精神障碍的大学年龄青年 由于担心耻辱和隐私、不便和等待时间，仍然不接受治疗，以及 因为大学往往无法为所有这样的学生提供服务。此外，至关重要的是， 实施转诊治疗时，不考虑患者的病情， 因为错误的假设认为治疗不需要针对 个人的。这项提议旨在解决这一关键的临床问题。我们提出了一项 复杂的自动在线推荐系统可以解决所有这些问题，但没有 精神科转诊的专家培训系统。我们建议将推荐过程自动化， 专为大学年龄的年轻人设计，通过在线沟通，心理健康评估和策划， 最新的精神健康提供者网络。为此，非营利性的儿童心灵 1nInstitute正在与营利性的MiResource合作。提供评估专业知识 儿童心理研究所，该研究所治疗儿童和青少年的心理健康 精神障碍，进行心理健康研究，获得大量评估数据集，拥有内部 在精神健康评估方面的专业知识，并通过其物质实验室开发了新的评估 诸如Mindlogger数据收集和评估平台等技术。转诊 基础设施由MiResource提供，这是一种软件即服务解决方案，旨在帮助 大学将学生与当地的心理健康提供者联系起来。物质实验室和MiResources 将开发一个自动化的在线评估和推荐平台，使用经过专家培训的 机器学习为用户提供个性化的心理健康护理转诊。 专家转诊将基于护理利用水平系统(风险)的六个方面 危害、功能状态、合并症、环境、治疗历史和态度) 适用于大学生对心理健康评估的反应。1N阶段1，我们将(1-1) 将心理健康评估构建到Mindlogger平台中，(1-2)构建专家 推荐收集接口，以及(1-3)建立机器学习管道，用于培训和 测试可更新的分类模型，以实现临床适用、个性化的自动化 推荐人。1N阶段11，我们将构建、改进和临床验证我们的 商业化的产品。具体地说，我们将(11-1)在 大学人口，(11-2)将Mindlogger的评估整合到MiResource中，以及(11-3) 对新的Mindlogger Plus MiResource平台进行可用性和质量保证测试， 为了获得有关可访问性、相关性、准确性和美观性问题的反馈， 并将针对此反馈的解决方案纳入最终版本。

This proposal addresses a significant barrier to obtaining treatment for college-age youth with mental disorders. Many college-age youth with impairing mental disorders remain untreated because of concerns about stigma and privacy, inconvenience and wait times, and because universities are often unable to service all such students. Also, of critical importance, when referral for treatment is implemented, it is without regard to the person's pathology, because of the erroneous assumption that treatment need not be tailored to the individual. This proposal aims to address this critical clinical issue. We advance that a sophisticated automated online referral system would resolve all of these problems, but there is no expert-trained system for psychiatric referrals. We propose to automate the referral process, designed for college-age youth, by bridging online, mental health assessments and curated, up-to-date, mental health provider networks. To this end, the non-profit Child Mind 1nstitute is partnering with the for-profit MiResource. Assessment expertise is provided by the Child Mind Institute, which treats children and adolescents with mental health disorders, conducts mental health research, has acquired large assessment datasets, has in-house expertise in mental health assessment, and through its MATTER lab has developed novel assessment technologies such as the Mindlogger data collection and assessment platform. Referral infrastructure is provided by MiResource, a software-as-a-service solution designed to help universities connect students to local mental health providers. The MATTER lab and MiResource will develop an automated online assessment and referral platform that uses expert-trained machine learning to provide users with personalized referrals for mental health care. Expert referrals will be based on the six dimensions of the level of Care Utilization System (risk of harm, functional status, comorbidity, environment, treatment history, and attitude) applied to college students' responses to mental health assessments. 1n Phase 1, we will (1-1) build mental health assessments into the Mindlogger platform, (1-2) build an expert referral collection interface, and (1-3) set up a machine learning pipeline for training and testing an updatable classification model for automated clinically appropriate, personalized referrals. 1n Phase 11, we will build, refine, and clinically validate our product for commercialization. Specifically, we will (11-1) validate the Phase I framework on a university population, (11-2) integrate Mindlogger's assessments into MiResource, and (11-3) conduct usability and quality assurance tests of the new Mindlogger plus MiResource platform, to get feedback about issues related to accessibility, relevance, accuracy, and esthetics, and incorporate solutions in response to this feedback into a final version.