SALT LAKE CITY

盐湖城

,

，

April 29, 2025

2025年4月29日

/PRNewswire/ -- Researchers at the

/PRNewswire/ -- 研究人员在

University of Utah's

犹他大学的

Department of Psychiatry and Huntsman Mental Health Institute today published a paper introducing

精神病学系和亨茨曼心理健康研究所今天发表了一篇论文介绍

RiskPath

风险路径

, an open source software toolkit that uses Explainable Artificial Intelligence (XAI), to predict whether individuals will develop progressive and chronic diseases years before symptoms appear, potentially transforming how preventive healthcare is delivered. XAI is an artificial intelligence system that can explain complex decisions in ways humans can understand..

，一个使用可解释人工智能（XAI）的开源软件工具包，可以在症状出现前几年预测个体是否会患上进展性和慢性疾病，这可能彻底改变预防性医疗保健的提供方式。XAI 是一种可以以人类能够理解的方式解释复杂决策的人工智能系统。

The new technology represents a significant advancement in disease prediction and prevention by analyzing patterns in health data collected over multiple years to identify at-risk individuals with unprecedented accuracy of 85-99%. Current medical prediction systems for longitudinal data often miss the mark, correctly identifying at-risk patients only about half to three-quarters of the time.

新技术通过分析多年收集的健康数据中的模式，在疾病预测和预防方面取得了重大进展，能够以前所未有的85%-99%的准确率识别高风险个体。当前用于纵向数据的医学预测系统通常准确性不足，正确识别高风险患者的比例仅约为一半到四分之三。

RiskPath uses advanced timeseries AI algorithms and makes them explainable in order to deliver comprehensive models that provide crucial insights into how risk factors interact and change in importance throughout the disease development process..

RiskPath 使用先进的时序人工智能算法，并使它们具有可解释性，以提供全面的模型，从而深入洞察风险因素在疾病发展过程中如何相互作用并改变重要性。

'Chronic, progressive diseases account for over 90% of healthcare costs and mortality,' said lead researcher

“慢性、进行性疾病占医疗费用和死亡率的90%以上，”首席研究员说。

Nina de Lacy

尼娜·德·拉西

, MD. 'By identifying high-risk individuals before symptoms appear or early in the disease course and pinpointing which risk factors matter most at different life stages, we can develop more targeted and effective preventive strategies. Preventative healthcare is perhaps the most important aspect of healthcare right now, rather than only treating issues after they materialize.'.

，医学博士。“通过在症状出现之前或疾病早期识别高风险个体，并确定在不同生命阶段哪些风险因素最为重要，我们可以制定更有针对性和更有效的预防策略。预防性医疗保健或许是目前医疗保健中最重要的方面，而不是仅在问题出现后才进行治疗。”

The research team validated RiskPath across three major long-term patient cohorts involving thousands of participants to successfully predict eight different conditions, including depression, anxiety, ADHD, hypertension, and metabolic syndrome. The technology offers several key advantages:

研究团队在涉及数千名参与者的三个主要长期患者队列中验证了RiskPath，成功预测了包括抑郁症、焦虑症、注意力缺陷多动障碍（ADHD）、高血压和代谢综合征在内的八种不同疾病。该技术提供了几个关键优势：

Enhanced Understanding of Disease Progression

对疾病进展的深入理解

: RiskPath can map how different risk factors change in importance over time, revealing critical windows for intervention. For example, the study showed how screen time and executive function become increasingly important risk contributors for ADHD as children approach adolescence.

RiskPath可以绘制不同风险因素随时间变化的重要性，揭示干预的关键窗口。例如，研究表明，随着儿童接近青春期，屏幕时间和执行功能对注意力缺陷多动障碍（ADHD）的风险贡献变得越来越重要。

Streamlined Risk Assessment

简化风险评估

: Though RiskPath can analyze hundreds of health variables, researchers found that most conditions can be predicted with similar accuracy using just 10 key factors, making implementation more feasible in clinical settings.

尽管 RiskPath 可以分析数百个健康变量，但研究人员发现，大多数情况只需使用 10 个关键因素即可达到类似的预测准确性，这使得在临床环境中实施更加可行。

Practical Risk Visualization

实用风险可视化

: The system provides intuitive visualizations showing which time periods in a person's life contribute most to disease risk, helping researchers identify optimal times for preventive interventions.

该系统提供了直观的可视化图表，显示一个人生命中哪些时间段对疾病风险的贡献最大，帮助研究人员确定预防干预的最佳时机。

The research team is now exploring how RiskPath could be integrated into clinical decision support systems, preventive care programs, and the neural underpinnings of mental illness. They plan to expand their research to include additional diseases and diverse populations.

研究团队现在正在探索如何将 RiskPath 整合到临床决策支持系统、预防保健计划以及精神疾病的神经基础中。他们计划扩展研究，以涵盖更多疾病和多样化的人群。

The full study on RiskPath was published in the April issue of CellPress

关于RiskPath的完整研究发表在四月的《CellPress》期刊上。

Patterns

模式

, and can be found

，可以找到

here

这里

. The research was led by

。该研究由

Nina de Lacy

尼娜·德·拉西

,

，

Michael Ramshaw

迈克尔·拉姆肖

, and

，以及

Wai Yin Lam

林慧妍

from the Department of Psychiatry at the

来自精神病学系的

University of Utah

犹他大学

. De Lacy serves on the

德莱西在

One-U Responsible AI Initiative Executive Committee

一U负责任人工智能倡议执行委员会

. The work was supported by the National Institute of Mental Health.

这项工作得到了国家心理健康研究所的支持。

About Huntsman Mental Health Institute

关于亨茨曼心理健康研究所

Huntsman Mental Health Institute at the

汉茨曼心理健康研究所

University of Utah

犹他大学

is a first-of-its-kind model created to address one of our nation's greatest challenges: mental health and substance use disorders. The institute combines the strength of one of America's leading research universities with the nation's best integrated mental health crisis care model and a comprehensive continuum of care that includes a 161-bed hospital and more than 85 outpatient locations.

是一个首创的模型，旨在应对我国最大的挑战之一：心理健康和物质使用障碍。该研究所结合了美国领先的研究型大学之一的实力，以及全国最佳的综合心理健康危机护理模式，并提供包括一家161张床位的医院和85个以上的门诊地点在内的全面连续护理体系。

We educate hundreds of learners every year and provide both unique and wide-ranging educational opportunities in psychiatry and mental health. Our innovative approach to research uses 'teams of teams' to bring together different disciplines to uncover new ways to tackle complex problems. A gift of .

我们每年教育数百名学习者，并在精神病学和心理健康领域提供独特且广泛的教学机会。我们创新的研究方法采用“团队中的团队”模式，汇聚不同学科的力量，以发掘解决复杂问题的新途径。一份礼物。

$150 million

1.5亿美元

from the Huntsman family helps power our mission to advance mental health knowledge, hope, and healing for all.

来自亨茨曼家族的支持助力我们推动全体心理健康知识、希望和康复的使命。

SOURCE Huntsman Mental Health Institute

来源：亨茨曼心理健康研究所

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