Jinbo Zhang, Pingping Yang, Lu Zeng, Shan Li, Jiamei Zhou
{"title":"基于人工智能的呼吸机相关肺炎预测模型:范围界定综述","authors":"Jinbo Zhang, Pingping Yang, Lu Zeng, Shan Li, Jiamei Zhou","doi":"10.2196/57026","DOIUrl":null,"url":null,"abstract":"Background: Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patient treatment and prognosis. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective: This article reviews the prediction models for VAP based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension guidelines. The Wanfang, Chinese BioMedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase databases were searched to identify relevant articles. Study selection and data extraction were independently conducted by two reviewers. The data extracted from the included studies were synthesized narratively. Results: From 137 publications, 11 were included in the scoping review. The included studies reported the use of AI for predicting VAP. All of the 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were used as the primary data choice for model building (6/11, 55 %), whereas the remaining studies had sample sizes smaller than 1000. Machine learning is the primary algorithm for studying the VAP prediction models. However, deep learning and large language models are not used to construct VAP prediction models. Random forest is the most commonly used algorithm (5/11, 45 %). All studies are internal validations, and none of them address how the model is used. Conclusions: This review presents an overview of studies based on AI used to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide an indispensable tool for the risk prediction of VAP in the future. However, the current research is in the stage of model construction and validation, and the implementation and guidance for the clinical prediction of VAP require further research.","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review\",\"authors\":\"Jinbo Zhang, Pingping Yang, Lu Zeng, Shan Li, Jiamei Zhou\",\"doi\":\"10.2196/57026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patient treatment and prognosis. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective: This article reviews the prediction models for VAP based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension guidelines. The Wanfang, Chinese BioMedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase databases were searched to identify relevant articles. Study selection and data extraction were independently conducted by two reviewers. The data extracted from the included studies were synthesized narratively. Results: From 137 publications, 11 were included in the scoping review. The included studies reported the use of AI for predicting VAP. All of the 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were used as the primary data choice for model building (6/11, 55 %), whereas the remaining studies had sample sizes smaller than 1000. Machine learning is the primary algorithm for studying the VAP prediction models. However, deep learning and large language models are not used to construct VAP prediction models. Random forest is the most commonly used algorithm (5/11, 45 %). All studies are internal validations, and none of them address how the model is used. Conclusions: This review presents an overview of studies based on AI used to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide an indispensable tool for the risk prediction of VAP in the future. However, the current research is in the stage of model construction and validation, and the implementation and guidance for the clinical prediction of VAP require further research.\",\"PeriodicalId\":56334,\"journal\":{\"name\":\"JMIR Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/57026\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/57026","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review
Background: Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patient treatment and prognosis. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP. Objective: This article reviews the prediction models for VAP based on AI, providing a reference for the early identification of high-risk groups in future clinical practice. Methods: A scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension guidelines. The Wanfang, Chinese BioMedical Literature Database, Cochrane Library, Web of Science, PubMed, MEDLINE, and Embase databases were searched to identify relevant articles. Study selection and data extraction were independently conducted by two reviewers. The data extracted from the included studies were synthesized narratively. Results: From 137 publications, 11 were included in the scoping review. The included studies reported the use of AI for predicting VAP. All of the 11 studies predicted VAP occurrence, and studies on VAP prognosis were excluded. Further, these studies used text data, and none of them involved imaging data. Public databases were used as the primary data choice for model building (6/11, 55 %), whereas the remaining studies had sample sizes smaller than 1000. Machine learning is the primary algorithm for studying the VAP prediction models. However, deep learning and large language models are not used to construct VAP prediction models. Random forest is the most commonly used algorithm (5/11, 45 %). All studies are internal validations, and none of them address how the model is used. Conclusions: This review presents an overview of studies based on AI used to predict and diagnose VAP. AI models have better predictive performance than traditional methods and are expected to provide an indispensable tool for the risk prediction of VAP in the future. However, the current research is in the stage of model construction and validation, and the implementation and guidance for the clinical prediction of VAP require further research.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.