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Recent developments in machine learning modeling methods for hypertension treatment

Hypertension Research volume 47, pages 700–707 (2024)Cite this article

Abstract

Hypertension is the leading cause of cardiovascular complications. This review focuses on the advancements in medical artificial intelligence (AI) models aimed at individualized treatment for hypertension, with particular emphasis on the approach to time-series big data on blood pressure and the development of interpretable medical AI models. The digitalization of daily blood pressure records and the downsizing of measurement devices enable the accumulation and utilization of time-series data. As mainstream blood pressure data shift from snapshots to time series, the clinical significance of blood pressure variability will be clarified. The time-series blood pressure prediction model demonstrated the capability to forecast blood pressure variabilities with a reasonable degree of accuracy for up to four weeks in advance. In recent years, various explainable AI techniques have been proposed for different purposes of model interpretation. It is essential to select the appropriate technique based on the clinical aspects; for example, actionable path-planning techniques can present individualized intervention plans to efficiently improve outcomes such as hypertension. Despite considerable progress in this field, challenges remain, such as the need for the prospective validation of AI-driven interventions and the development of comprehensive systems that integrate multiple AI methods. Future research should focus on addressing these challenges and refining the AI models to ensure their practical applicability in real-world clinical settings. Furthermore, the implementation of interdisciplinary collaborations among AI experts, clinicians, and healthcare providers are crucial to further optimizing and validate AI-driven solutions for hypertension management.

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Authors and Affiliations

  1. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Hirohiko Kohjitani, Hiroshi Koshimizu, Kazuki Nakamura & Yasushi Okuno

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  1. Hirohiko Kohjitani
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  2. Hiroshi Koshimizu
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  3. Kazuki Nakamura
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  4. Yasushi Okuno
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Correspondence to Hirohiko Kohjitani or Yasushi Okuno.

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Employment: HirosKos (Omron Healthcare Co., Ltd.); Research fund: HirohKoh, YO (Omron Healthcare Co., Ltd.).

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Kohjitani, H., Koshimizu, H., Nakamura, K. et al. Recent developments in machine learning modeling methods for hypertension treatment. Hypertens Res 47, 700–707 (2024). https://doi.org/10.1038/s41440-023-01547-w

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