Suffolk Reporter

Two researchers at Stony Brook University are working on using machine learning models to predict patient outcomes. Richard N. Rosenthal, MD, a professor in the Department of Psychiatry and Behavioral Health in the Renaissance School of Medicine, and Fusheng Wang, PhD, a professor in the departments of Biomedical Informatics and Computer Science in both the RSOM and College of Engineering and Applied Sciences, are focusing their efforts on optimizing risk prediction related to opioid use disorder and overdose.

Their research is backed by a $1.05 million grant from the Patient-Centered Outcomes Research Institute (PCORI), an independent organization funding patient-centered comparative clinical effectiveness research across the United States.

Wang's work centers on developing models that assess patients' likelihood of developing opioid-related issues. The goal is to create a practical machine learning tool for clinicians to anticipate patient risks and tailor treatment plans accordingly. The process involves extracting data from patients' medical records for predictions.

"Most AI model development in health care is done by the developers so that there is little if any feedback into the process by the end users, such as clinicians," said Rosenthal. "As a result, because of how uncurated machine learning works, the doctors are frequently left with non-intuitive models that they can’t explain to patients for making treatment decisions, so most models are underutilized."

Rosenthal highlighted that their collaborative approach introduces what they call a "stakeholder-in-the-loop approach," enabling clinicians to provide input to enhance model accuracy and usability, thus making it more patient-centric.

"I think probably the most important contribution in this type of model is our stakeholder-in-the-loop approach," added 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 that is missing. If we can provide a framework with this particular model, the lessons learned can be very useful for others to adopt a similar methodology."

The complexity of patient data presents challenges due to numerous clinical variables involved. The stakeholder-in-the-loop method allows clinicians to integrate clinical insights into predictions.

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

The project includes collaboration among patient partners, clinicians, computer scientists, researchers from various institutions including New York State Office of Mental Health and Suffolk County Department of Health.

The team aims to apply this research method beyond opioid-related disorders to other diseases like cardiovascular conditions and test its application in clinical settings such as emergency departments.