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Authors: Daniel Hijosa-Guzmán 1 ; María Teresa Jurado-Camino 1 ; Pablo de Miguel-Bohoyo 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): Temporal Patterns, Clinical Decision Support, Chronic Conditions, Patient Disease Trajectory, Pharmaceutical Treatment.

Abstract: Chronic diseases are one of the leading causes of death worldwide, with diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease among the most common ones. In this sense, the extraction of clinical patterns from the data recorded in the Electronic Health Record is of great interest and motivates research in models to predict the temporal evolution of the patient’s health status. Predictive models would be of great help in the treatment of chronic patients to carry out preventive policies. Our approach considers the Gated Recurrent Unit neural network to extract temporal patterns of drug dispensation and to predict the progression of Chronic Conditions (CCs) towards a more complex health status. Real-world data linked to chronic patients of a Spanish hospital were considered, obtaining the most probable health status among a set of 10, including single dominant or moderate CCs, significant CCs in multiple organ systems, and dominant CC in three or m ore organ systems. Accuracy rates above 70% for single dominant or moderate CCs and nearly 50% for significant/dominant conditions across multiple organs were obtained. These results show the potential of sequential networks to predict the clinical risk of chronic patients and support clinical decision-making. (More)

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Paper citation in several formats:
Hijosa-Guzmán, D.; Teresa Jurado-Camino, M.; de Miguel-Bohoyo, P. and Mora-Jiménez, I. (2024). Sequential Networks for Predicting the Clinical Risk of Chronic Patients Using Drug Dispensation. 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 37-44. DOI: 10.5220/0012321800003657

@conference{healthinf24,
author={Daniel Hijosa{-}Guzmán. and María {Teresa Jurado{-}Camino}. and Pablo {de Miguel{-}Bohoyo}. and Inmaculada Mora{-}Jiménez.},
title={Sequential Networks for Predicting the Clinical Risk of Chronic Patients Using Drug Dispensation},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={37-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012321800003657},
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 - Sequential Networks for Predicting the Clinical Risk of Chronic Patients Using Drug Dispensation
SN - 978-989-758-688-0
IS - 2184-4305
AU - Hijosa-Guzmán, D.
AU - Teresa Jurado-Camino, M.
AU - de Miguel-Bohoyo, P.
AU - Mora-Jiménez, I.
PY - 2024
SP - 37
EP - 44
DO - 10.5220/0012321800003657
PB - SciTePress