The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method
In recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filter...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2022/6761444 |
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| _version_ | 1849306890806231040 |
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| author | Qian Lu Wei Zhao Zhongpeng Li Ranfeng Liu |
| author_facet | Qian Lu Wei Zhao Zhongpeng Li Ranfeng Liu |
| author_sort | Qian Lu |
| collection | DOAJ |
| description | In recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filtering technologies are developing rapidly, and more and more are applied to different fields. Based on the deep learning technology, this paper conducts in-depth research on the methods of assisted diagnosis of clinical diseases and prediction of clinical high-risk diseases in the field of CDSS. Aiming at the problem of clinical decision support system, this article analyzes the deep learning identification method in depth and is committed to applying machine deep learning to clinical decision-making, changing the lack of information and its challenges to clinical decision-making. Based on previous studies, two unsupervised learning methods based on machine learning are proposed, namely user collaborative filtering and RBM, to improve CDSS. The experimental results show that the overall performance of the RBM-based method is the best. When the missing degree of the two data sets is 30.6%, the classification accuracy rate is still more than 92.8%. |
| format | Article |
| id | doaj-art-1dda3b40a1974c97ab1d7b4fdbfab6e9 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-1dda3b40a1974c97ab1d7b4fdbfab6e92025-08-20T03:54:56ZengWileyInternational Transactions on Electrical Energy Systems2050-70382022-01-01202210.1155/2022/6761444The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition MethodQian Lu0Wei Zhao1Zhongpeng Li2Ranfeng Liu3Information DepartmentInformation DepartmentInformation DepartmentInformation DepartmentIn recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filtering technologies are developing rapidly, and more and more are applied to different fields. Based on the deep learning technology, this paper conducts in-depth research on the methods of assisted diagnosis of clinical diseases and prediction of clinical high-risk diseases in the field of CDSS. Aiming at the problem of clinical decision support system, this article analyzes the deep learning identification method in depth and is committed to applying machine deep learning to clinical decision-making, changing the lack of information and its challenges to clinical decision-making. Based on previous studies, two unsupervised learning methods based on machine learning are proposed, namely user collaborative filtering and RBM, to improve CDSS. The experimental results show that the overall performance of the RBM-based method is the best. When the missing degree of the two data sets is 30.6%, the classification accuracy rate is still more than 92.8%.http://dx.doi.org/10.1155/2022/6761444 |
| spellingShingle | Qian Lu Wei Zhao Zhongpeng Li Ranfeng Liu The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method International Transactions on Electrical Energy Systems |
| title | The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method |
| title_full | The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method |
| title_fullStr | The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method |
| title_full_unstemmed | The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method |
| title_short | The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method |
| title_sort | design of critical care information system supporting clinical decision based on deep learning recognition method |
| url | http://dx.doi.org/10.1155/2022/6761444 |
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