Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice
Uncertainty natural language processing has always been a research focus in the artificial intelligence field. In this paper, we continue to study the linguistic truth-valued concept lattice and apply it to the disease intelligent diagnosis by building an intelligent model to directly handle natural...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6630077 |
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| author | Li Yang Yuhui Wang Haixia Li |
| author_facet | Li Yang Yuhui Wang Haixia Li |
| author_sort | Li Yang |
| collection | DOAJ |
| description | Uncertainty natural language processing has always been a research focus in the artificial intelligence field. In this paper, we continue to study the linguistic truth-valued concept lattice and apply it to the disease intelligent diagnosis by building an intelligent model to directly handle natural language. The theoretical bases of this model are the classical concept lattice and the lattice implication algebra with natural language. The model includes the case library formed by patients, attributes matching, and the matching degree calculation about the new patient. According to the characteristics of the patients, the disease attributes are firstly divided into intrinsic invariant attributes and extrinsic variable attributes. The calculation algorithm of the linguistic truth-valued formal concepts and the constructing algorithm of the linguistic truth-valued concept lattice based on the extrinsic attributes are proposed. And the disease bases of the different treatments for different patients with the same disease are established. Secondly, the matching algorithms of intrinsic attributes and extrinsic attributes are given, and all the linguistic truth-valued formal concepts that match the new patient’s extrinsic attributes are found. Lastly, by comparing the similarity between the new patients and the matching formal concepts, we calculate the best treatment options to realize the intelligent diagnosis of the disease. |
| format | Article |
| id | doaj-art-5065163c445447f5ab68334a44eac16e |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5065163c445447f5ab68334a44eac16e2025-02-03T05:52:27ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66300776630077Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept LatticeLi Yang0Yuhui Wang1Haixia Li2School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaCollege of Mechanical & Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, ChinaDepartment of General Education, Anhui Xinhua University, Hefei 230088, ChinaUncertainty natural language processing has always been a research focus in the artificial intelligence field. In this paper, we continue to study the linguistic truth-valued concept lattice and apply it to the disease intelligent diagnosis by building an intelligent model to directly handle natural language. The theoretical bases of this model are the classical concept lattice and the lattice implication algebra with natural language. The model includes the case library formed by patients, attributes matching, and the matching degree calculation about the new patient. According to the characteristics of the patients, the disease attributes are firstly divided into intrinsic invariant attributes and extrinsic variable attributes. The calculation algorithm of the linguistic truth-valued formal concepts and the constructing algorithm of the linguistic truth-valued concept lattice based on the extrinsic attributes are proposed. And the disease bases of the different treatments for different patients with the same disease are established. Secondly, the matching algorithms of intrinsic attributes and extrinsic attributes are given, and all the linguistic truth-valued formal concepts that match the new patient’s extrinsic attributes are found. Lastly, by comparing the similarity between the new patients and the matching formal concepts, we calculate the best treatment options to realize the intelligent diagnosis of the disease.http://dx.doi.org/10.1155/2021/6630077 |
| spellingShingle | Li Yang Yuhui Wang Haixia Li Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice Complexity |
| title | Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice |
| title_full | Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice |
| title_fullStr | Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice |
| title_full_unstemmed | Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice |
| title_short | Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice |
| title_sort | research on the disease intelligent diagnosis model based on linguistic truth valued concept lattice |
| url | http://dx.doi.org/10.1155/2021/6630077 |
| work_keys_str_mv | AT liyang researchonthediseaseintelligentdiagnosismodelbasedonlinguistictruthvaluedconceptlattice AT yuhuiwang researchonthediseaseintelligentdiagnosismodelbasedonlinguistictruthvaluedconceptlattice AT haixiali researchonthediseaseintelligentdiagnosismodelbasedonlinguistictruthvaluedconceptlattice |