PDF(Pubmed)
Abstract：
OBJECTIVE: The Sequential Organ Failure Assessment (SOFA) score plays a crucial role in intensive care units (ICUs) by providing a reliable measure of a patient\'s organ function or extent of failure. However, the precise assessment is time-consuming, and daily assessment in clinical practice in the ICU can be challenging.
METHODS: Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman\'s as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score.
RESULTS: The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate (r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94.
CONCLUSIONS: Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.
METHODS: Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman\'s as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score.
RESULTS: The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate (r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94.
CONCLUSIONS: Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.
摘要：
目的：序贯器官衰竭评估（SOFA）评分在重症监护病房（ICU）中起着至关重要的作用，可以提供患者器官功能或衰竭程度的可靠指标。然而,精确的评估是耗时的，ICU临床实践中的日常评估可能具有挑战性。
方法：创建了ICU环境中的真实场景，和ChatGPT4.0Plus的数据挖掘精度，巴德,使用Spearman's以及关于确定SOFA评分的准确性的组内相关系数评估了困惑AI。
结果：在实际SOFA评分与通过ChatGPT4.0Plus计算的评分之间观察到最强的相关性（r相关系数0.92）（p<0.001）。相比之下,实际SOFA与Bard计算的SOFA之间的相关性中等（r=0.59，p=0.070），虽然与困惑AI的相关性很大，在0.89，p<0.001。SOFA与ChatGPT4.0Plus的类间相关系数分析，巴德,困惑AI为ICC=0.94。
结论：人工智能（AI）工具，特别是ChatGPT4.0Plus，在ICU设置中通过AI数据挖掘协助自动进行SOFA分数计算方面表现出重大希望。它们提供了减少人工工作量并提高连续患者监测和评估效率的途径。然而,进一步的开发和验证是必要的，以确保在重症监护环境的准确性和可靠性。
方法：创建了ICU环境中的真实场景，和ChatGPT4.0Plus的数据挖掘精度，巴德,使用Spearman's以及关于确定SOFA评分的准确性的组内相关系数评估了困惑AI。
结果：在实际SOFA评分与通过ChatGPT4.0Plus计算的评分之间观察到最强的相关性（r相关系数0.92）（p<0.001）。相比之下,实际SOFA与Bard计算的SOFA之间的相关性中等（r=0.59，p=0.070），虽然与困惑AI的相关性很大，在0.89，p<0.001。SOFA与ChatGPT4.0Plus的类间相关系数分析，巴德,困惑AI为ICC=0.94。
结论：人工智能（AI）工具，特别是ChatGPT4.0Plus，在ICU设置中通过AI数据挖掘协助自动进行SOFA分数计算方面表现出重大希望。它们提供了减少人工工作量并提高连续患者监测和评估效率的途径。然而,进一步的开发和验证是必要的，以确保在重症监护环境的准确性和可靠性。
