Leveraging deep learning and clinical notes for surveillance and prediction of intentional self-harm and suicide

利用深度学习和临床记录来监测和预测故意自残和自杀

基本信息

  • 批准号:
    10330113
  • 负责人:
  • 金额:
    $ 55.18万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-05-01 至 2023-04-30
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY: Suicide is one of the leading causes of death in the United States, with more than 47,000 individuals dying by suicide each year. The identification of individuals at risk for suicide is an important step for a comprehensive approach to suicide prevention. Despite extensive research on risk factors for intentional self-harm and suicide, prospective prediction of suicide remains a difficult task with poor predictive power. Recent studies suggest that new machine learning methods applied to electronic health records (EHR) show promising results. However, more advanced computational approaches such as deep learning, have not been fully leveraged in this field, especially in the area of advanced methods for text classification of clinical notes. Our aims in this project, are to improve the phenotyping of suicidal behavior, and the prediction of future suicidal behavior and suicide deaths by integrating mortality data with EHR data and leveraging state-of-the-art natural language computational approaches. We will also investigate methods for explain ability and interpretability of the models to improve future adoption by clinicians. We will validate our models by examining reproducibility and generalizability across two health systems using similar data at both sites.
项目概要: 自杀是美国的主要死亡原因之一, 每年都有人自杀。识别有自杀风险的个人是一项 这是全面预防自杀重要一步。尽管进行了广泛的研究 关于故意自伤和自杀的危险因素,自杀的前瞻性预测仍然是一个 预测能力差的困难任务。最近的研究表明,新机器学习 应用于电子健康记录(EHR)的方法显示出有希望的结果。但更多 先进的计算方法,如深度学习,尚未充分利用, 这一领域,特别是在临床笔记的文本分类的先进方法领域。我们 该项目的目的是改善自杀行为的表型, 通过将死亡率数据与EHR数据相结合, 利用最先进的自然语言计算方法。我们亦会研究 模型的解释能力和可解释性的方法,以提高未来的采用率, 临床医生我们将通过检查跨区域的可重复性和可推广性来验证我们的模型。 两个卫生系统在两个地点使用类似的数据。

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)

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Jihad S Obeid其他文献

Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review
心理健康研究中的自然语言处理与健康的社会决定因素:人工智能辅助范围审查
  • DOI:
    10.2196/67192
  • 发表时间:
    2025-01-01
  • 期刊:
  • 影响因子:
    5.800
  • 作者:
    Dmitry A Scherbakov;Nina C Hubig;Leslie A Lenert;Alexander V Alekseyenko;Jihad S Obeid
  • 通讯作者:
    Jihad S Obeid

Jihad S Obeid的其他文献

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{{ truncateString('Jihad S Obeid', 18)}}的其他基金

Investigating teleconsent to improve clinical research access in remote communities
研究远程同意以改善偏远社区的临床研究机会
  • 批准号:
    9389723
  • 财政年份:
    2017
  • 资助金额:
    $ 55.18万
  • 项目类别:
FUNCTIONAL ANALYSIS OF 11B-HYDROXYSTEROID DEHYDROGENASE
11B-羟基类固醇脱氢酶的功能分析
  • 批准号:
    3037617
  • 财政年份:
    1992
  • 资助金额:
    $ 55.18万
  • 项目类别:
FUNCTIONAL ANALYSIS OF 11B-HYDROXYSTEROID DEHYDROGENASE
11B-羟基类固醇脱氢酶的功能分析
  • 批准号:
    3037618
  • 财政年份:
    1992
  • 资助金额:
    $ 55.18万
  • 项目类别:

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