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A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea

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Abstract

Study objectives

Obstructive sleep apnea (OSA) is a major concern in modern medicine; however, it is difficult to diagnose. Screening questionnaires such as the Berlin questionnaire, Rome questionnaire, and BASH'IM score are used to identify patients with OSA. However, the sensitivity and specificity of these tools are not satisfactory. We aim to introduce an artificial intelligence method to screen moderate to severe OSA patients (apnea–hypopnea index ≧15).

Patients and methods

One hundred twenty patients were asked to complete a newly developed questionnaire before undergoing an overnight polysomnography (PSG) study. One hundred ten validated questionnaires were enrolled in this study. Genetic algorithm (GA) was used to build the five best models based on these questionnaires. The same data were analyzed with logistic regression (LR) for comparison.

Results

The sensitivity of the GA models varied from 81.8% to 88.0%, with a specificity of 95% to 97%. On the other hand, the sensitivity and specificity of the LR model were 55.6% and 57.9%, respectively.

Conclusions

GA provides a good solution to build models for screening moderate to severe OSA patients, who require PSG evaluation and medical intervention. The questionnaire did not require any special biochemistry data and was easily self-administered. The sensitivity and specificity of the GA models are satisfactory and may improve when more patients are recruited.

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Acknowledgments

This work was performed at the Taipei Medical University Hospital and Sleep Center of Far Eastern Medical Hospital, Taipei, Taiwan. No external funding was obtained.

Disclosure

No financial support; no off-label or investigational use.

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Authors and Affiliations

  1. Graduate Institute of Medical Informatics, Taipei Medical University, 252, Wu-Hsin Street, Taipei, 110, Taiwan

    Lei Ming Sun, Hung-Wen Chiu & Li Liu

  2. Sleep Center of Far Eastern Medical Hospital, Taipei, Taiwan

    Chih Yuan Chuang

Authors
  1. Lei Ming Sun
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  2. Hung-Wen Chiu
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  3. Chih Yuan Chuang
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  4. Li Liu
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Corresponding author

Correspondence to Li Liu.

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Sun, L.M., Chiu, HW., Chuang, C.Y. et al. A prediction model based on an artificial intelligence system for moderate to severe obstructive sleep apnea. Sleep Breath 15, 317–323 (2011). https://doi.org/10.1007/s11325-010-0384-x

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  • DOI: https://doi.org/10.1007/s11325-010-0384-x

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