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严重和更昂贵之前，主动识别和解决10 - 18岁儿童的心理健康问题。该项目创新提出在需要时使用聊天机器人伴侣为儿童提供护理，这些伴侣专门针对每个儿童的心理健康需求而定制。可选地，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，学校或独自一人。该系统利用经过验证的移动的设备访问的认知行为疗法与机器学习（ML）技术相结合，这些技术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，焦虑和抑郁体验，并在认为有必要进行干预和治疗时提醒成年人。如果没有适当的护理，这些症状的严重程度往往会随着时间的推移而增加，并且变得更加难以治疗。早期干预可以帮助减缓或停止精神疾病，减少父母，学校和社会与反应性，普遍的精神保健治疗相关的实际和经济负担，同时培养孩子更快乐，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，儿童在获得精神保健时面临的障碍比成年人更多，特别是在农村，边缘化和社会经济地位低的社区。由于全国儿童行为健康从业人员严重短缺，儿童在出现心理健康症状和接受治疗之间往往要等待长达十年。该项目旨在消除儿童第一次体验精神或情感需求与他们获得适当照顾之间的延迟，确保他们在这些关键的发展时期能够适应而不是挣扎。利用机器学习算法和专有问题权重，该项目专注于：1）算法开发，以确定聊天机器人和实时聊天顾问参与的最有效组合，以了解孩子的紧迫问题，并为孩子及其父母提供解决方案; 2）改进，以确保响应符合目的，识别和标记适当的对话以进行人类互动;以及3）改进通知的频率和内容;例如，自适应激励信息和建议辅导员定制互动。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估。

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.