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.

Points of Research

• Using clinical data, build a model with high prediction accuracy using machine learning.
• It is expected that it will be used in the clinical field by showing the possibility of exceeding conventional statistical models.
• Research that serves as a bridge between healthcare and data science.

Author's Comment

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

Paper information

• Title of the 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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