2022 Fiscal Year Final Research Report
Big data analytics on artificial intelligence technologies for cardiovascular risk stratification
| Project/Area Number |
20H03681
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Review Section |
Basic Section 53020:Cardiology-related
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
西村 邦宏 国立研究開発法人国立循環器病研究センター, 研究所, 部長 (70397834)
野口 暉夫 国立研究開発法人国立循環器病研究センター, 病院, 副院長 (70505099)
泉 知里 国立研究開発法人国立循環器病研究センター, 病院, 部門長 (70768100)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 循環器病 / 人工知能 / 器械学習 / バイオカーマー / 予測医療 / 先制医療 / 心不全 |
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| Outline of Final Research Achievements |
Risk prediction for heart failure (HF) using machine learning methods (MLM) has not yet been established at practical application levels in clinical settings. This study aimed to create a new risk prediction model for HF with a minimum number of predictor variables using MLM
In the patients with HF (n = 987), CCEs occurred in 142 patients. In the testing dataset, the substantial predictive power of the MLM-risk model was obtained (AUC = 0.87). We generated the model using 15 variables. Our MLM-risk model showed superior predictive power in the prospective study compared to conventional risk models such as the Seattle Heart Failure Model (c-statistics: 0.86 vs. 0.68, p < 0.05). Notably, the model with an input variable number (n = 5) has comparable predictive power for CCE with the model (variable number = 15). This study developed and validated a model with minimized five variables to predict mortality more accurately in patients with HF, using a MLM, than the existing risk scores.
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| Free Research Field |
循環器学
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| Academic Significance and Societal Importance of the Research Achievements |
超高齢化社会を迎えたわが国において心筋梗塞・心不全などの循環器系疾患の克服は重要な課題の一つです。これらの疾患は一度発症すると軽快と増悪を繰り返しながら進行しQOLの低下のみならず介護・医療費の増大を招き社会全体に大きな負担増をもたらします。疾患発症前の予測・予防（簡便で精度の高いリスク予測モデル）、またハイリスク患者の早期同定（新たなバイオマーカーの応用と計測）のため人工知能を用いた新たな手法開発を行いました。
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