SBIR Phase I: An Automated Assistant for Mental Health

SBIR 第一阶段:心理健康自动化助手

基本信息

  • 批准号:
    1345452
  • 负责人:
  • 金额:
    $ 15万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-01-01 至 2014-06-30
  • 项目状态:
    已结题

项目摘要

This SBIR Phase I project proposes to address the challenges medical professionals face since the signing of The Patient Protection and Affordable Care Act. Today's mental health therapist must prepare for an increased patient load as the pool of insured Americans grows, while simultaneously reducing the overall cost of healthcare. The healthcare delivery process must be streamlined by eliminating unnecessary tests, procedures, and repeat patient care. This project will provide therapists tools to: (1) speed up and improve patient diagnosis, (2) prepare a course of treatment that is likely to yield a fast and positive patient outcome, and (3) keep informed about scientific findings that directly impacting their daily work. Deep research issues need to be solved to parse the technical jargon of medical literature and reconcile that with the free form narrative typical in therapist session notes. The project will produce novel methods to transform patient records and medical resources like the Diagnostic and Statistical Manual of Mental Disorders (DSM) into a medically tuned semantic graph that is merged with a rich mental health ontology. Moreover, the project will research advanced text similarity algorithms to align patient data with DSM disorders, quality treatment plan options, and relevant research findings. Broader Impacts/ Commercial Potential: The broader/commercial impact of this project is to deliver an automated medical assistant to mental health professionals that will give them time to focus on patient interactions and make healthcare more personal. Government agencies and insurance companies will benefit as increased efficiency and quality of care from therapists will lead to lower healthcare costs, especially because mental health disorders often manifest themselves as general medical conditions. The World Health Organization reports that mental disorders account for nearly 12% of the global burden of disease, and that by 2020 these disorders will account for nearly 15% of disability-adjusted life-years lost to illness. Further because the burden of mental disorders is maximal in young adults, the most productive section of the population, improvements to mental health diagnosis and treatment will significantly impact the American society as a whole. The patient population will also benefit from lowered personal costs as well as a lessened societal burden that comes with taking care of the mentally ill.
该SBIR I阶段项目提议以解决自《患者保护和负担得起的护理法》签署以来的医疗专业人员面临的挑战。当今的心理健康治疗师必须准备增加患者的负担,因为被保险人的美国人增长,同时降低了医疗保健的总体成本。必须通过消除不必要的测试,程序和重复患者护理来简化医疗保健过程。该项目将为治疗师提供以下工具:(1)加快并改善患者诊断,(2)准备可能产生快速和积极的患者结果的治疗方法,以及(3)继续了解直接影响其日常工作的科学发现。深层研究问题需要解决以解析医学文献的技术术语,并将其与治疗师会议笔记中典型的自由形式进行调和。该项目将产生新的方法来转变患者记录和医疗资源,例如精神障碍的诊断和统计手册(DSM),成为医学调整的语义图,并与丰富的心理健康本体论合并。此外,该项目将研究高级文本相似算法,以使患者数据与DSM疾病,质量治疗计划选择和相关研究结果相结合。更广泛的影响/商业潜力:该项目的更广泛/商业影响是为心理健康专业人员提供自动医疗助理,这将使他们有时间专注于患者互动并使医疗保健更加个性化。政府机构和保险公司将受益,因为从治疗师那里提高效率和护理质量将导致降低医疗费用,尤其是因为精神健康疾病通常表现为一般医疗状况。世界卫生组织报告说,精神障碍占全球疾病负担的近12%,到2020年,这些疾病将占残疾调整后的终身年份的近15%。此外,由于精神障碍的负担在年轻人中是最大的人群中最大的,因此对心理健康诊断和治疗的改善将对整个美国社会产生重大影响。患者群体还将受益于降低的个人成本以及照顾精神病患者的社会负担减轻。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

暂无数据

数据更新时间:2024-06-01

Mithun Balakrishna其他文献

Knowledge extraction for literature review
文献综述知识提取
Automatic Building of Semantically Rich Domain Models from Unstructured Data
从非结构化数据自动构建语义丰富的领域模型
  • DOI:
  • 发表时间:
    2013
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mithun Balakrishna;D. Moldovan
    Mithun Balakrishna;D. Moldovan
  • 通讯作者:
    D. Moldovan
    D. Moldovan
N-Best List Reranking using Higher Level Phonetic, Lexical, Syntactic and Semantic Knowledge Sources
使用高级语音、词汇、句法和语义知识源的 N 最佳列表重新排序
Ten ways of leveraging ontologies for natural language processing and its enterprise applications
利用本体进行自然语言处理及其企业应用程序的十种方法
共 4 条
  • 1
前往

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  • 批准号:
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  • 财政年份:
    2007
  • 资助金额:
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  • 项目类别:
    Standard Grant
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