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

Ying-Jen Chang, Kuo-Chuan Hung, Li-Kai Wang, Chia-Hung Yu, Chao-Kun Chen, Hung-Tze Tay, Jhi-Joung Wang and Chung-Feng Liu
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Ying-Jen Chang: Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
Kuo-Chuan Hung: Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan
Li-Kai Wang: Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, 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
Chung-Feng Liu: Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan

IJERPH, 2021, vol. 18, issue 5, 1-14

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.

Keywords: lung resection; pulmonary function test; artificial intelligence; machine learning; pre-anesthetic consultation; staged weaning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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