{"title":"乌特斯坦式院内心脏骤停智能诊疗系统的实施:多中心案例研究。","authors":"Yan Shao, Zhou Yang, Wei Chen, Yingqi Zhang","doi":"10.1186/s12967-024-05792-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.</p><p><strong>Methods: </strong>To compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.</p><p><strong>Results: </strong>The system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3-9 and 9.9-23, respectively.</p><p><strong>Conclusions: </strong>The intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"22 1","pages":"996"},"PeriodicalIF":6.1000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536878/pdf/","citationCount":"0","resultStr":"{\"title\":\"Implementing an intelligent diagnosis and treatment system for in-hospital cardiac arrest in the Utstein style: a multi-center case study.\",\"authors\":\"Yan Shao, Zhou Yang, Wei Chen, Yingqi Zhang\",\"doi\":\"10.1186/s12967-024-05792-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Cardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.</p><p><strong>Methods: </strong>To compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.</p><p><strong>Results: </strong>The system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3-9 and 9.9-23, respectively.</p><p><strong>Conclusions: </strong>The intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.</p>\",\"PeriodicalId\":17458,\"journal\":{\"name\":\"Journal of Translational Medicine\",\"volume\":\"22 1\",\"pages\":\"996\"},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2024-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536878/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Translational Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12967-024-05792-6\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, RESEARCH & EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Translational Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12967-024-05792-6","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
Implementing an intelligent diagnosis and treatment system for in-hospital cardiac arrest in the Utstein style: a multi-center case study.
Background: Cardiac arrest presents a variety of causes and complexities, making it challenging to develop targeted treatment plans. Often, the original data are either inadequate or lack essential patient information. In this study, we introduce an intelligent system for diagnosing and treating in-hospital cardiac arrest (IHCA), aimed at improving the success rate of cardiopulmonary resuscitation and restoring spontaneous circulation.
Methods: To compensate for insufficient or incomplete data, a hybrid mega trend diffusion method was used to generate virtual samples, enhancing system performance. The core of the system is a modified episodic deep reinforcement learning module, which facilitates the diagnosis and treatment process while improving sample efficiency. Uncertainty analysis was performed using Monte Carlo simulations, and dependencies between different parameters were assessed using regular vine copula. The system's effectiveness was evaluated using ten years of data from Utstein-style IHCA registries across seven hospitals in China's Hebei Province.
Results: The system demonstrated improved performance compared to other models, particularly in scenarios with inadequate data or missing patient information. The average reward scores in two key stages increased by 2.3-9 and 9.9-23, respectively.
Conclusions: The intelligent diagnosis and treatment effectively addresses IHCA, providing reliable diagnosis and treatment plans in IHCA scenarios. Moreover, it can effectively induce cardiopulmonary resuscitation and restoration of spontaneous circulation processes even when original data are insufficient or basic patient information is missing.
期刊介绍:
The Journal of Translational Medicine is an open-access journal that publishes articles focusing on information derived from human experimentation to enhance communication between basic and clinical science. It covers all areas of translational medicine.