Reposted from U of U Health.

University of Utah researchers have unveiled a new open-source software toolkit that uses artificial intelligence to predict whether individuals will develop progressive and chronic diseases years before symptoms appear, potentially transforming how preventive health care is delivered.

RiskPath is a new technology that advances disease prediction and prevention by analyzing patterns in health data collected over multiple years to identify at-risk individuals with unprecedented accuracy of 85 to 99%, according to research published this week by members of the U’s Department of Psychiatry and Huntsman Mental Health Institute.

The program uses Explainable Artificial Intelligence, an AI system designed to explain complex decisions in ways humans can understand. 

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. Unlike existing prediction systems for longitudinal data, RiskPath uses advanced time-series AI algorithms that deliver crucial insights into how risk factors interact and change in importance throughout the disease process.

“Chronic, progressive diseases account for over 90% of health care costs and mortality,” said research leader Nina de Lacy, a professor of psychiatry. “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 health care is perhaps the most important aspect of health care right now, rather than only treating issues after they materialize.”

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:

• Enhanced Understanding of Disease Progression: 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.
• Streamlined Risk Assessment: 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.
• Practical Risk Visualization: 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.

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.

The study was published in the April issue of CellPress Patterns under the title “RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.” Co-authors included Michael Ramshaw and Wai Yin Lam of the Department of Psychiatry. De Lacy serves on the One-U Responsible AI Initiative Executive Committee. The work was supported by the National Institute of Mental Health.

• Patricia Brandt PR/Communications Manager Huntsman Mental Health Institute
  Patricia.Brandt@hsc.utah.edu