A central problem in machine learning is to learn from data (``big''...
AI in Medicine Advances with BU-MIT Research Team
Researchers from Boston University and the Massachusetts Institute of Technology have pioneered an AI method that learns from existing data how to make specific recommendations (“prescriptions”) to optimize a certain outcome. Their research findings have been published in this month in PLOS ONE.
The paper titled “Prescriptive Analytics for Reducing 30-day Hospital Readmissions after General Surgery” proposes an innovative solution for reducing re-hospitalization rates. Combining predictive and prescriptive statistical modeling, researchers Ioannis (Yannis) Paschalidis and Taiyao Wang of Boston University, and Dimitris Bertsimas and Michael Lingzhi Li of MIT, identify the factors behind re-hospitalization rates and suggest an effective treatment: targeted preoperative blood transfusions.
“The vast majority of AI in medicine develops models that learn how to predict some outcome,” says Dr. Paschalidis, Professor of the College of Engineering (ECE, BME, SE, CDS) and Director of the Center for Information & Systems Engineering at BU. “What is novel in this work is that we train a model that offers personalized recommendations/prescriptions aimed at improving patient outcomes. The approach has broad applicability and the potential to improve medical decision making.”
The method results from thorough research and application. Using data from more than 700,000 surgeries from the American College of Surgeons National Surgical Quality Improvement Program (NSQIP), the researchers analyzed how pre-operative factors (such as underlying medical conditions) and post-operative factors (including complications from surgery) affect current re-hospitalization rates.
By incorporating both pre-operative and post-operative factors in their prescriptive models, the researchers discovered pre-operative hematocrit levels significantly affected re-hospitalization rates. From there, they created a prescriptive model that suggested targeted pre-operative transfusions to increase hematocrit. As a result, the blood transfusions reduced the likelihood of a re-hospitalization.
As it stands, the U.S. spends over $3 trillion annually on healthcare, with a large portion resulting from re-hospitalization rates. However, with the presented research, that number could fall. The researchers estimate a 12% decrease in 30-day rehospitalization rates if one were to follow the model, potentially saving the U.S. over $20 million on an annual basis.
“The use of AI in medicine is likely to increase, helping to improve patient outcomes and reduce costs,” adds Dr. Paschalidis. “This paper is an example of how AI could be used to affect modifiable factors that improve outcomes through personalized medicine.”
To learn more about the research and methods of this publication, read the full paper here.
PLOS ONE is the world’s first multidisciplinary Open Access journal. The journal’s publication criteria are based on high ethical standards and the rigor of the methodology and conclusions reported.
By Maria Yaitanes, CISE Staff