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Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile

Author

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  • Manuel Casal-Guisande

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
    Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • María Torres-Durán

    (Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
    NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • Mar Mosteiro-Añón

    (Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
    NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • Jorge Cerqueiro-Pequeño

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
    Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • José-Benito Bouza-Rodríguez

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
    Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • Alberto Fernández-Villar

    (Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
    NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

  • Alberto Comesaña-Campos

    (Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
    Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain)

Abstract

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.

Suggested Citation

  • Manuel Casal-Guisande & María Torres-Durán & Mar Mosteiro-Añón & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Fernández-Villar & Alberto Comesaña-Campos, 2023. "Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile," IJERPH, MDPI, vol. 20(4), pages 1-31, February.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3627-:d:1072710
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    References listed on IDEAS

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    1. Alberto Comesaña-Campos & Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez, 2020. "A Methodology Based on Expert Systems for the Early Detection and Prevention of Hypoxemic Clinical Cases," IJERPH, MDPI, vol. 17(22), pages 1-31, November.
    2. Jorge Cerqueiro-Pequeño & Alberto Comesaña-Campos & Manuel Casal-Guisande & José-Benito Bouza-Rodríguez, 2020. "Design and Development of a New Methodology Based on Expert Systems Applied to the Prevention of Indoor Radon Gas Exposition Risks," IJERPH, MDPI, vol. 18(1), pages 1-32, December.
    3. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
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    1. Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Comesaña-Campos, 2023. "Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems," Mathematics, MDPI, vol. 11(11), pages 1-33, May.

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