Wang and Rosenthal Awarded $1.05M for Patient Risk Prediction Model Research

The Patient-Centered Outcomes Research Institute (PCORI) has announced a $1.05 million award to Fusheng Wang, professor in the Departments of Biomedical Informaticsand Computer Science, and his team, including Richard Rosenthal, professor and addiction psychiatrist from the Department of Psychiatry, MPI of the project. Their research is focused on machine learning models to predict 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 focusing on improving methods in research focused around patients. Wang's research on using machine learning to predict patient risk was one of the studies granted an award.

Wang's research focuses on creating models to predict how likely patients are to develop opioid use disorder and opioid overdose. Every individual patient varies in opioid risk. Using a prediction model, Wang and his team seek to develop a tool for clinicians to foresee patient risk.

This machine model is designed to help predict patient outcomes which clinicians can use to influence their course of treatment. The machine learning model pulls from patient records to make a prediction. What is revolutionary about Wang's research is what he calls the "stakeholder in the loop approach," where clinicians can provide feedback to the prediction model to make the output more accurate. This approach makes 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. I think if we can, through our study, if we can provide a framework, the lessons learned can be very useful for others to adopt a similar methodology."

One of the challenges that Wang and his team have to overcome is that patient data is very complex, with lots of clinical variables. What variables contribute to the prediction of the risk are unclear. This is another reason why the stakeholder in the loop approach is important because it gives clinicians the chance to add in clinical knowledge.

Wang and his team have to take this complex model and make it user friendly where clinicians not only understand what the machine is saying, but also put their own knowledge into it. The output then has to be concise and easy to understand, and can be 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 the patient, why the patient is predicted with such a risk."

What really sets apart the research is how many people with different areas of expertise are involved. "The project brings in patient partners, clinicians, computer scientists, researchers and community representatives from the New York State Office of Mental Health and Suffolk County Department of Health to collaborate together," said Rosenthal.

In the long term, Wang hopes to expand his research for other diseases, including those of the heart. He also hopes to implement the model in a clinical setting, such as an emergency department, to test out the system to its fullest extent.

- Angelina Livigni