A paper in which Professor Kazumi Kubota of Faculty of Data Science participated as a collaborator was published in the Circulation Journal (2024 Impact Factor: 3.7).

In this study, we analyzed the risk of perioperative cardiovascular events in patients undergoing gastrointestinal surgery using a predictive model using machine learning. We showed the possibility of improving preoperative evaluation and perioperative management by stratifying high-risk patients before surgery.

Point of Research

• Using clinical data to build a model with high prediction accuracy using machine learning.
• It is expected that it will exceed the conventional statistical model and will be used in clinical settings.
• Research that serves as a bridge between medical care and data science.

Author's Comment

“Complications associated with surgery are a huge risk for patients. We believe that by applying the knowledge of data science, we can build a safer management system at medical sites. Our strengths are small but interdisciplinary research, and it is attractive to be able to develop practical research in collaboration with clinical sites."

Paper information

• Title of Paper: Evaluation of Perioperative Cardiovascular Event Risk in Gastrointestinal Surgery — Predictive Modeling and Risk Stratification Using Machine Learning
• Author: Ito H, Seki T, Kawazoe Y, Takiguchi T, Akagi Y, Kubota K, Miyake K, Okada M, Ohe K.
• Publications: Circulation Journal
• Date of publication: April 24, 2025 (online pre-published)
• DOI: 10.1253/circj.CJ-25-0032

[Contact information]
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