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

doi:10.3390/ijerph18052713

Chia Nan University of Pharmacy & Science Institutional Repository > 中心級單位 > 通識教育中心 > 期刊論文 > Item 310902800/34455

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://ir.cnu.edu.tw/handle/310902800/34455

標題:	A Real-Time Artificial Intelligence-Assisted System to Predict Weaning from Ventilator Immediately after Lung Resection Surgery
作者:	Chang, Ying-Jen Hung, Kuo-Chuan Wang, Li-Kai Yu, Chia-Hung Chen, Chao-Kun Tay, Hung-Tze Wang, Jhi-Joung Liu, Chung-Feng
貢獻者:	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
關鍵字:	lung resection pulmonary function test artificial intelligence machine learning pre-anesthetic consultation staged weaning
日期:	2021
上傳時間:	2023-11-11 11:53:44 (UTC+8)
出版者:	MDPI
摘要:	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.
關聯:	INT J ENV RES PUB HE, v.18, n.5, pp.2713
显示于类别:	[通識教育中心] 期刊論文

文件中的档案:

档案	描述	大小	格式	浏览次数
ijerph18052713.pdf		2383Kb	Adobe PDF	150	检视/开启
index.html		0Kb	HTML	234	检视/开启

在CNU IR中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....