{"title":"使用人工智能算法预测COVID-19大流行期间血液透析患者的总体生存:一项前瞻性队列研究","authors":"Shao-Yu Tang, Tz-Heng Chen, Ko-Lin Kuo, Jue-Ni Huang, Chen-Tsung Kuo, Yuan-Chia Chu","doi":"10.1097/JCMA.0000000000000994","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients.</p><p><strong>Methods: </strong>A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts.</p><p><strong>Results: </strong>Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies.</p><p><strong>Conclusion: </strong>This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.</p>","PeriodicalId":17251,"journal":{"name":"Journal of the Chinese Medical Association","volume":" ","pages":"1020-1027"},"PeriodicalIF":1.9000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.\",\"authors\":\"Shao-Yu Tang, Tz-Heng Chen, Ko-Lin Kuo, Jue-Ni Huang, Chen-Tsung Kuo, Yuan-Chia Chu\",\"doi\":\"10.1097/JCMA.0000000000000994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients.</p><p><strong>Methods: </strong>A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts.</p><p><strong>Results: </strong>Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies.</p><p><strong>Conclusion: </strong>This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.</p>\",\"PeriodicalId\":17251,\"journal\":{\"name\":\"Journal of the Chinese Medical Association\",\"volume\":\" \",\"pages\":\"1020-1027\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Chinese Medical Association\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/JCMA.0000000000000994\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/9/15 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Chinese Medical Association","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/JCMA.0000000000000994","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/15 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.
Background: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients.
Methods: A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts.
Results: Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies.
Conclusion: This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.
期刊介绍：
Journal of the Chinese Medical Association, previously known as the Chinese Medical Journal (Taipei), has a long history of publishing scientific papers and has continuously made substantial contribution in the understanding and progress of a broad range of biomedical sciences. It is published monthly by Wolters Kluwer Health and indexed in Science Citation Index Expanded (SCIE), MEDLINE®, Index Medicus, EMBASE, CAB Abstracts, Sociedad Iberoamericana de Informacion Cientifica (SIIC) Data Bases, ScienceDirect, Scopus and Global Health.
JCMA is the official and open access journal of the Chinese Medical Association, Taipei, Taiwan, Republic of China and is an international forum for scholarly reports in medicine, surgery, dentistry and basic research in biomedical science. As a vehicle of communication and education among physicians and scientists, the journal is open to the use of diverse methodological approaches. Reports of professional practice will need to demonstrate academic robustness and scientific rigor. Outstanding scholars are invited to give their update reviews on the perspectives of the evidence-based science in the related research field. Article types accepted include review articles, original articles, case reports, brief communications and letters to the editor
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