{"title":"使用机器学习的危重病人风险预测和严重程度的实时临床决策系统","authors":"Ammanath Gopal, M. Sailatha, S. Vikas, G. Sampath","doi":"10.1109/ICIRCA51532.2021.9544943","DOIUrl":null,"url":null,"abstract":"This paper predicts the diseases of the patients by considering their symptoms who are admitted in the critical care units. This system operates at the bed side of the patients and predicts the diseases so that the basic treatment is provided to the patients without any delay. By providing basic medication to the patients, the occurrence of serious conditions and circumstances can be prevented. In hospitals, there is a decision system that operates using three phase approach which is prone to delay and inaccuracy. The proposed system eradicates the inaccurate and delayed results by considering the moderate datasets and hence yields better and fast results.","PeriodicalId":245244,"journal":{"name":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Real Time Clinical Decision System for Risk Prediction and Severity in Critical Ill Patients Using Machine Learning\",\"authors\":\"Ammanath Gopal, M. Sailatha, S. Vikas, G. Sampath\",\"doi\":\"10.1109/ICIRCA51532.2021.9544943\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper predicts the diseases of the patients by considering their symptoms who are admitted in the critical care units. This system operates at the bed side of the patients and predicts the diseases so that the basic treatment is provided to the patients without any delay. By providing basic medication to the patients, the occurrence of serious conditions and circumstances can be prevented. In hospitals, there is a decision system that operates using three phase approach which is prone to delay and inaccuracy. The proposed system eradicates the inaccurate and delayed results by considering the moderate datasets and hence yields better and fast results.\",\"PeriodicalId\":245244,\"journal\":{\"name\":\"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIRCA51532.2021.9544943\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIRCA51532.2021.9544943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Real Time Clinical Decision System for Risk Prediction and Severity in Critical Ill Patients Using Machine Learning
This paper predicts the diseases of the patients by considering their symptoms who are admitted in the critical care units. This system operates at the bed side of the patients and predicts the diseases so that the basic treatment is provided to the patients without any delay. By providing basic medication to the patients, the occurrence of serious conditions and circumstances can be prevented. In hospitals, there is a decision system that operates using three phase approach which is prone to delay and inaccuracy. The proposed system eradicates the inaccurate and delayed results by considering the moderate datasets and hence yields better and fast results.