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

doi:10.3390/ijerph18052713

CNU IR > The Centers > The Center For General Education > Periodical Articles > Item 310902800/34455

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34455

Title:	A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery
Authors:	Chang, Ying-Jen Hung, Kuo-Chuan Wang, Li-Kai Yu, Chia-Hung Chen, Chao-Kun Tay, Hung-Tze Wang, Jhi-Joung Liu, Chung-Feng
Contributors:	Chi Mei Med Ctr, Dept Anesthesiol Chang Jung Christian Univ, Coll Hlth Sci Chia Nan Univ Pharm & Sci, Gen Educ Ctr Chi Mei Med Ctr, Dept Thorac Surg Chi Mei Med Ctr, Dept Intens Care Med Chi Mei Med Ctr, Dept Med Res Chi Mei Med Ctr, Dept Med Res, Ctr Big Med Data & Artificial Intelligence Comp
Keywords:	lung resection pulmonary function test artificial intelligence machine learning pre-anesthetic consultation staged weaning
Date:	2021
Issue Date:	2023-11-11 11:53:44 (UTC+8)
Publisher:	MDPI
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 Naive 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.
Relation:	INT J ENV RES PUB HE, v.18, n.5, pp.2713
Appears in Collections:	[The Center For General Education] Periodical Articles

Files in This Item:

File	Description	Size	Format
ijerph18052713.pdf		2383Kb	Adobe PDF	37	View/Open
index.html		0Kb	HTML	96	View/Open

Loading...