Suffolk Reporter

The Patient-Centered Outcomes Research Institute (PCORI) has awarded $1.05 million to Fusheng Wang, a professor in the departments of Biomedical Informatics and Computer Science, and his team. The team includes Richard Rosenthal, MD, a professor and addiction psychiatrist from the Department of Psychiatry. Their research aims to develop machine learning models for predicting patient outcomes.

“Deep learning is revolutionizing risk prediction models in health care by providing unprecedented accuracy and insights. As we integrate these advanced techniques into our systems, building credibility and trust through rigorous research and transparent processes is essential,” said Samir Das, professor and chair of the Department of Computer Science. “By demonstrating the robustness, reliability and explainability of these models, we not only enhance the quality of patient care but also foster confidence in the transformative power of machine learning.”

PCORI recently approved funding awards for studies focused on improving patient-centered research methods. Wang’s study on using machine learning to predict patient risk was among those granted an award.

Wang’s research specifically targets creating models to predict patients' likelihood of developing opioid use disorder and opioid overdose. The goal is to develop a tool that clinicians can use to foresee patient risk based on individual variability.

This machine model pulls data from patient records to make predictions about outcomes that can influence treatment decisions. A notable aspect of Wang’s research is what he calls the “stakeholder-in-the-loop approach,” where clinicians provide feedback to improve model accuracy. This method aims to make the machine learning model more human-centric.

“I think probably the most important contribution is the stakeholder-in-the-loop approach,” said Wang. “Stakeholders, including clinicians and patients, will participate in the full cycle of model design, development and evaluation. I think for the health care domain, that’s really something missing. We don’t see anybody doing something systematically like us."

One challenge faced by Wang's team is managing complex patient data with numerous clinical variables whose contributions to risk prediction are unclear. The stakeholder-in-the-loop approach allows clinicians to add their clinical knowledge into the model.

The objective is also to ensure that this complex model remains user-friendly so that clinicians understand its outputs while integrating their own expertise into it. The output must be concise, easy-to-understand, and effectively communicated to patients.

“A doctor wants to know all the information as quickly as possible, as comprehensive as possible,” said Wang. “If the machine learning model generates a prediction, then we need to really have a good precise summary about why the patient is predicted with such a risk.”

The project involves various experts including patient partners, clinicians, computer scientists, researchers, community representatives from New York State Office of Mental Health and Suffolk County Department of Health collaborating together," noted Rosenthal.

In future phases, Wang hopes his research can be expanded beyond opioid risks to other diseases such as heart conditions. He also plans to implement this predictive model in clinical settings like emergency departments for thorough testing.

— Angelina Livigni