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Authors: Laura Martínez-Marquina 1 ; María Teresa Jurado-Camino 1 ; Isabel Caballero-López-Fando 2 and Inmaculada Mora-Jiménez 1

Affiliations: 1 Dept. Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Madrid, Spain ; 2 University Hospital of Fuenlabrada, Madrid, Spain

Keyword(s): Interpretability, Time-Dependent Patterns, Clinical Codes, Sex-Based Model, Clinical Decision Support.

Abstract: Chronic diseases have emerged as a pervasive global health concern, standing as a leading cause of mortality. Among these, prevalent conditions encompass diabetes, hypertension, congestive heart failure and chronic obstructive pulmonary disease. The large amount of data in Electronic Health Records is being exploited by machine learning schemes to design clinical decision support systems, usually of limited practical application because of lack of transparency. To overcome this issue and given the dynamic nature of the health-status over time, we propose here a patient health monitoring scheme based on a Näive Bayes approach because of its interpretability, minimal computational cost, and efficient handling of high-dimensional and unbalanced data. Our approach considers clinical codes (diagnosis and drugs) on real data collected by a Spanish hospital and provides a probability score for different chronic health-statuses. A gender-based approach has also been explored, exhibiting prom ising performance when there is a significant patient population for each sex. We conclude that pharmacological codes are more informative, although the best performance was obtained by using all the clinical codes and demographic features. Though a more exhaustive study on patient monitoring is necessary, the proposed NB scheme can be considered a proof of concept which has demonstrated to be a valuable tool and easily interpretable method. (More)

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Paper citation in several formats:
Martínez-Marquina, L.; Teresa Jurado-Camino, M.; Caballero-López-Fando, I. and Mora-Jiménez, I. (2024). Naïve Bayes as a Probabilistic Tool for Monitoring the Health Status of Chronic Patients. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 393-403. DOI: 10.5220/0012353000003657

@conference{healthinf24,
author={Laura Martínez{-}Marquina. and María {Teresa Jurado{-}Camino}. and Isabel Caballero{-}López{-}Fando. and Inmaculada Mora{-}Jiménez.},
title={Naïve Bayes as a Probabilistic Tool for Monitoring the Health Status of Chronic Patients},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={393-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012353000003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Naïve Bayes as a Probabilistic Tool for Monitoring the Health Status of Chronic Patients
SN - 978-989-758-688-0
IS - 2184-4305
AU - Martínez-Marquina, L.
AU - Teresa Jurado-Camino, M.
AU - Caballero-López-Fando, I.
AU - Mora-Jiménez, I.
PY - 2024
SP - 393
EP - 403
DO - 10.5220/0012353000003657
PB - SciTePress