SBIR Phase I: An intelligent mental health care companion for kids

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是在治疗和后果变得更加严重和成本显著增加之前，主动识别和解决10 - 18岁儿童的心理健康问题。这个项目的创新之处是，在孩子需要的时候就给他们提供照顾，使用专门针对每个孩子的心理健康需求量身定制的聊天机器人伴侣。可选的是，孩子可以与实时聊天治疗师和教练配对，无论孩子在哪里——在家里、学校还是独自一人。该系统利用经过验证的移动设备访问的认知行为疗法与机器学习（ML）技术相结合，从与儿童的各种互动中学习，以检测和数字化分类他们的孤独、愤怒、焦虑和抑郁经历，并在必要的干预和治疗时提醒成年人。如果没有适当的护理，这些症状往往会随着时间的推移而加重，变得更加难以治疗。早期干预可以帮助减缓或阻止精神疾病，减少家长、学校和社会因被动的、普遍的精神保健治疗而带来的实际和经济负担，同时将儿童培养成更快乐、更健康的成年人。这个小企业创新研究（SBIR）第一阶段项目旨在建立一个系统，通过实时、早期检测来确定儿童心理健康需求的性质和严重程度。研究表明，儿童在获得心理保健方面面临的障碍比成人更多，特别是在农村、边缘化和社会经济地位较低的社区。由于全国儿童行为健康从业人员严重短缺，儿童经常在出现精神健康症状和接受治疗之间等待长达十年。该项目旨在消除儿童第一次经历精神或情感需求和接受适当照顾之间的延迟，确保他们在这些关键的发育时期茁壮成长，而不是挣扎。利用机器学习算法和专有问题加权，该项目专注于：1)算法开发，以确定聊天机器人和实时聊天咨询服务的最有效组合，以了解孩子的即时问题，并为孩子和他们的父母提供解决方案；2)改进以确保响应符合目的，识别和标记适合人类互动的适当对话；3)细化通知的频率和内容；例如，自适应的激励信息和建议，以调整辅导员的互动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to proactively identify and resolve mental health issues for children ages 10 - 18 before treatments and consequences become more acute and significantly more costly. This project innovation proposes bringing care to children at the onset of need using chatbot companions tailored deliberately and precisely to each child’s mental healthcare needs. Optionally, a child can be paired with live chat therapists and coaches no matter where the child is — at home, school, or when on their own. The system leverages proven mobile device-accessed Cognitive Behavioral Therapies in tandem with machine learning (ML)-enabled technologies that learn from a variety of interactions with the child to detect and digitally triage their experiences of loneliness, anger, anxiety, and depression and to alert adults when intervention and treatment are deemed necessary. Without appropriate care, these symptoms frequently increase in severity over time and become more difficult to treat. Intervening early can help slow or halt mental illness, reducing for parents, schools, and society the practical and financial burdens associated with reactive, generalized mental healthcare treatments while nurturing children into happier, healthier adults.This Small Business Innovation Research (SBIR) Phase I project seeks to build a system to determine the nature and severity of a child’s mental health needs through real-time, early detection. Research reveals children face more barriers than adults when obtaining mental healthcare, especially in rural, marginalized, and low-socioeconomic-status communities. Due to a severe shortage of child behavioral health practitioners across the country, children frequently wait up to a decade between the onset of mental health symptoms and treatment. This project seeks to eliminate the delay between when children first experience mental or emotional needs and when they receive appropriate care, ensuring they flourish—not flounder—during those crucial developmental years. Leveraging ML algorithms and proprietary question weighting, the project focuses on: 1) algorithm development to determine the most effective combination of chatbot and live chat counselor engagements to understand a child’s immediate issues and provide resolutions for the child and their parent(s); 2) improvements to ensure the responses are fit for purpose, recognizing and flagging appropriate conversations for human interaction; and 3) refining the frequency and content of notifications; such as, adaptive motivational messages and recommendations to counselors for tailoring interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.