SBIR Phase I: Automated Emotional Distress Severity Classification Using Speech Analytics and SFSS for SUD and OUD-Related ACE and Trauma

小型企业创新研究(SBIR)第一阶段项目将产生更广泛的影响/商业潜力，因为将重点放在社会经济地位较低的家庭，在这些家庭中，不良童年经历(ACE)和阿片类药物使用障碍(OUD)很常见。父母患有肥胖的儿童因忽视、身体虐待或家庭暴力而患血管紧张素转换酶的几率较高。ACE具有终身不良后果，包括药物滥用、早期残疾和死亡。通过使用创新技术改进对儿童和青少年心理健康问题的及时和客观测量，该公司旨在帮助医疗补助等提供者和公共资金来源实现以更低成本改善护理、质量和体验的三重目标。这些分析解决方案旨在帮助从事儿童和青少年工作的精神健康专业人员及早有效地识别问题和情绪障碍的严重性，并系统地跟踪治疗进展和结果。这个小型企业创新研究(SBIR)第一阶段项目旨在开发一种机器学习(ML)算法，从接受心理健康和家庭保护服务的高危青年的语音样本中检测临床相关的情绪困扰。在与两个服务于佐治亚州农村和城市家庭的社区行为健康组织进行初步合作后，该公司拥有一个基于云的运营数字健康平台，该平台将收集未成年人的声音样本与经过验证的青少年心理健康调查相结合。与佐治亚理工学院的语音和语音信号处理专家合作，早期工作提出了两个ML模型，将在拟议的项目中进一步开发和验证。在目标1中，症状和功能严重程度量表(SFSS)和语音数据将由治疗师在接受心理健康服务的社区样本中使用该公司的APP在护理点系统地收集。EMR临床数据将帮助对语音样本数据进行分类，以训练、验证和测试ML算法。在目标2中，独立评估者将观察并重新设计治疗师培训和实施方案的建议。该成果是一个以利益相关者为中心的行为医疗保健系统ML治疗结果跟踪平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 will result from a focus on families with low socioeconomic status (SES), in whom adverse childhood experiences (ACE) and opioid use disorder (OUD) are common. Children of parents with OUD have higher incidences of ACE due to neglect, physical abuse, or domestic violence. ACE have life-long adverse consequences, including drug abuse, early disability, and death. By improving the timely and objective measurement of mental health issues in children and adolescents using innovative technology, the company aims to help providers and public funding sources, such as Medicaid, meet the triple aim of improving care, quality and experience at a lower cost. The analytic solutions are intended to help mental health professionals working with children and adolescents identify problems and emotional disorder severity early and efficiently, and track treatment progress and outcomes systematically. This Small Business Innovation Research (SBIR) Phase I project aims to develop a machine learning (ML) algorithm that detects clinically relevant emotional distress in speech samples from at-risk youth receiving mental health and family preservation services. Following preliminary work with two community behavioral health organizations that serve rural and urban families in Georgia, the company has an operational cloud-based digital health platform that integrates the collection of voice samples from minors with a validated youth mental health survey. In collaboration with a voice and speech signal processing expert at the Georgia Institute of Technology, early work has advanced two ML models that will be further developed and validated in the proposed project. In Aim 1, Symptoms and Functioning Severity Scale (SFSS) and voice data will be systematically collected by therapists using the company app at the point of care in a community-based sample of youth receiving mental health services. EMR clinical data will help categorize voice sample data to train, validate, and test the ML algorithms. In Aim 2, independent evaluators will observe and make redesign recommendations of the therapist training and implementation protocol. The deliverable is a stakeholder centered ML treatment outcome tracking platform for the behavioral healthcare system.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.