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Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing

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Abstract

Purpose

Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.

Methods

This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.

Results

The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.

Conclusion

This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.

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Data availability

The database used in this study is available to all users within the Mendeley Data repository on demmand, with the following https://doi.org/10.17632/72xhjkvjk2.1.

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Acknowledgements

Not Applicable.

Funding

This research has received financial support from several sources: the 42nd edition of the nursing award of the University Pontificia Comillas and Escuela de Enfermeria y Fisioterapia San Juan de Dios; Carvascare Research Group from the Universidad de Castilla-La Mancha (2023-GRIN-34459); PID2021-128525OB-I00 and TED2021-130935B-I00, funded by the Spanish Government in conjunction with the European Regional Development Fund (EU) jointly with SBPLY/21/180501/000186, provided by the Junta de Comunidades de Castilla-La Mancha, Spain and the European Regional Development Fund (EU).

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Authors and Affiliations

  1. Informatics Systems Department, University of Castilla-La Mancha, Cuenca, Spain

    Arturo Martinez-Rodrigo

  2. Systems Automation and Engineering Department, Carlos III University of Madrid, Madrid, Spain

    Jose Carlos Castillo

  3. Health and Social Research Center, University of Castilla-La Mancha, Cuenca, Spain

    Alicia Saz-Lara, Iris Otero-Luis & Iván Cavero-Redondo

  4. Facultad de Ciencias de la Salud, Universidad Autonoma de Chile, Talca, Chile

    Iván Cavero-Redondo

Authors
  1. Arturo Martinez-Rodrigo
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  2. Jose Carlos Castillo
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  3. Alicia Saz-Lara
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  4. Iris Otero-Luis
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  5. Iván Cavero-Redondo
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Contributions

Conceptualization, IC-R and AS-L; methodology, AM-R and JCC; software, AM-R; validation, IO-L, AD-L and IC-R; formal analysis, AM-R and IC-R; investigation, AM-R; resources, IC-R and AS-L; data curation, IC-R and IO-L; writing—original draft preparation, AM-R and JCC; writing—review and editing, AM-R; visualization, AM-R, AS-L and IO-L; supervision, IC-R and AS-L. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Alicia Saz-Lara.

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Conflict of interest

The authors declares no conflict of interest.

Ethical approval and consent to participate

The research protocol of this study was approved by the Clinical Research Ethics Committee of the Cuenca Health Area (REG: 2022/PI2022). Written informed consent to participate was obtained from all subjects included in the study.

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Written informed consent for publication was obtained from all subjects included in the study.

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Martinez-Rodrigo, A., Castillo, J.C., Saz-Lara, A. et al. Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing. Health Inf Sci Syst 12, 34 (2024). https://doi.org/10.1007/s13755-024-00292-9

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