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实时人工智能辅助系统,用于预测肺切除术后立即脱离呼吸机。

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

机构信息

Department of Anesthesiology, Chi Mei Medical Center, Tainan 710, Taiwan.

College of Health Sciences, Chang Jung Christian University, Tainan 710, Taiwan.

出版信息

Int J Environ Res Public Health. 2021 Mar 8;18(5):2713. doi: 10.3390/ijerph18052713.

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.

摘要

肺切除术前风险评估可为麻醉师提供患者术后能否立即脱离呼吸机的信息。然而,麻醉师在有限的麻醉前门诊时间内很难进行全面的综合风险评估。我们回顾性地收集了 2017 年 1 月 1 日至 2019 年 7 月 31 日期间接受肺切除术的 709 名患者的电子病历。我们使用所获得的数据,构建了一个具有 7 种监督机器学习算法的人工智能(AI)预测模型,以预测患者在肺切除术后是否能立即脱离呼吸机。具有朴素贝叶斯分类器算法的 AI 模型具有最佳的测试结果,因此被用于开发一种应用程序,该应用程序可以根据患者的既往医疗数据评估风险,协助麻醉师,并在麻醉前门诊预测患者的预后。该 AI 应用程序的个性化和数字化特点可以提高风险解释和医患沟通的有效性,以实现更好的患者理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/453a/7967444/a328848ec4e1/ijerph-18-02713-g001.jpg

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