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A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery

Author

Listed:
  • Ying-Jen Chang

    (Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
    College of Health Sciences, Chang Jung Christian University, Tainan 710, Taiwan)

  • Kuo-Chuan Hung

    (Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
    General Education Center, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan)

  • Li-Kai Wang

    (Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
    General Education Center, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan)

  • Chia-Hung Yu

    (Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan)

  • Chao-Kun Chen

    (Department of Thoracic Surgery, Chi Mei Medical Center, Tainan 710, Taiwan)

  • Hung-Tze Tay

    (Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan 710, Taiwan)

  • Jhi-Joung Wang

    (Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
    Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan)

  • Chung-Feng Liu

    (Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan
    Center for Big Medical Data and Artificial Intelligence Computing, Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan)

Abstract

Assessment of risk before lung resection surgery can provide anesthesiologists with information about whether a patient can be weaned from the ventilator immediately after surgery. However, it is difficult for anesthesiologists to perform a complete integrated risk assessment in a time-limited pre-anesthetic clinic. We retrospectively collected the electronic medical records of 709 patients who underwent lung resection between 1 January 2017 and 31 July 2019. We used the obtained data to construct an artificial intelligence (AI) prediction model with seven supervised machine learning algorithms to predict whether patients could be weaned immediately after lung resection surgery. The AI model with Naïve Bayes Classifier algorithm had the best testing result and was therefore used to develop an application to evaluate risk based on patients’ previous medical data, to assist anesthesiologists, and to predict patient outcomes in pre-anesthetic clinics. The individualization and digitalization characteristics of this AI application could improve the effectiveness of risk explanations and physician–patient communication to achieve better patient comprehension.

Suggested Citation

  • Ying-Jen Chang & Kuo-Chuan Hung & Li-Kai Wang & Chia-Hung Yu & Chao-Kun Chen & Hung-Tze Tay & Jhi-Joung Wang & Chung-Feng Liu, 2021. "A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery," IJERPH, MDPI, vol. 18(5), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2713-:d:512671
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    References listed on IDEAS

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    Cited by:

    1. Ji Eun Park & Tae Young Kim & Yun Jung Jung & Changho Han & Chan Min Park & Joo Hun Park & Kwang Joo Park & Dukyong Yoon & Wou Young Chung, 2021. "Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success," IJERPH, MDPI, vol. 18(17), pages 1-17, September.
    2. Chien-Lung Chan & Chi-Chang Chang, 2022. "Big Data, Decision Models, and Public Health," IJERPH, MDPI, vol. 19(14), pages 1-9, July.

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