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. 2021 Mar 8;18(5):2713.
doi: 10.3390/ijerph18052713.

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

Ying-Jen Chang  1   2 , Kuo-Chuan Hung  1   3 , Li-Kai Wang  1   3 , Chia-Hung Yu  1 , Chao-Kun Chen  4 , Hung-Tze Tay  5 , Jhi-Joung Wang  1   6 , Chung-Feng Liu  6   7
Affiliations

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

Ying-Jen Chang et al. Int J Environ Res Public Health. .

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: artificial intelligence; lung resection; machine learning; pre-anesthetic consultation; pulmonary function test; staged weaning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Spearman correlation analysis (Heat map).
Figure 2
Figure 2
Receiver operating characteristic curve (ROC) of testing result.
Figure 3
Figure 3
Final application software of the AI predicting system, implanted in the computer of the preanesthetic clinic. (a) Screenshots of AI predicting system applied to a patient receiving neostigmine. (b) Screenshots of AI predicting system applied to a patient receiving sugammadex.
Figure 4
Figure 4
Time-saving percentage after using the AI system among anesthesiologists working in preanesthetic consulting clinic. (a) The subjective perception of junior anesthesiologists. (b) The subjective perception of senior anesthesiologists.
Figure 5
Figure 5
Patient satisfaction score for the understanding of anesthetic explanation, before and after AI system implantation.
See this image and copyright information in PMC

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