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

SBIR 第一阶段：使用语音分析和 SFSS 对 SUD 和 OUD 相关 ACE 和创伤进行自动情绪困扰严重程度分类

• 批准号: 1938206
• 负责人: Yared Alemu
• 金额: $ 22.5万
• 依托单位: TQIntelligence, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1938206&HistoricalAwards=false
• 关键词: SBIR; Phase; Automated; Emotional; Distress

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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于对低社会经济地位（SES）家庭的关注，在这些家庭中，不良童年经历（ACE）和阿片类药物使用障碍（OUD）是常见的。父母患有OUD的儿童由于忽视，身体虐待或家庭暴力而发生ACE的发生率较高。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善儿童和青少年心理健康问题的及时和客观测量，该公司旨在帮助提供商和公共资金来源，如医疗补助，以更低的成本实现改善护理，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心理健康专业人员尽早有效地识别问题和情绪障碍的严重程度，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，该算法可以检测接受心理健康和家庭保护服务的高危青年的语音样本中的临床相关情绪困扰。在与两个为格鲁吉亚农村和城市家庭提供服务的社区行为健康组织进行初步合作后，该公司拥有一个可运营的基于云的数字健康平台，该平台将未成年人声音样本的收集与经过验证的青少年心理健康调查集成在一起。与格鲁吉亚理工学院的语音和语音信号处理专家合作，早期的工作已经推进了两个ML模型，这些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使用公司应用程序在接受心理健康服务的青年社区样本的护理点进行系统收集。EMR临床数据将有助于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，独立评估人员将观察治疗师培训和实施方案，并提出重新设计建议。该奖项反映了NSF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。