Research on the Disease Intelligent Diagnosis Model Based on Linguistic Truth-Valued Concept Lattice

Li Yang,Yuhui Wang,Haixia Li,Heng Liu

doi:10.1155/2021/6630077

Abstract

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.

Highlights

Intelligent diagnosis technology began in the 1980s, it has become a very active research field, and its characteristic is to apply the technical achievements of artificial intelligence to the field of intelligent diagnosis
In order to make better use of artificial intelligence technology to solve the problem of disease intelligent diagnosis with uncertain natural language information, based on the previous work [15,16,17,18], this paper continues to study the linguistic truth-valued concept lattice (LTV-CL) and applies it to medical intelligent diagnosis
We can calculate the matching degree between the patients to be diagnosed and the matching formal concepts obtained by extrinsic attribute matching in the case database through the intrinsic attribute matching algorithm, find the linguistic truth-valued formal concept with the highest matching degree, and give a more specific diagnosis according to the specific matching degree by referring to the diagnosis scheme in the case database

Summary

Introduction

Intelligent diagnosis technology began in the 1980s, it has become a very active research field, and its characteristic is to apply the technical achievements of artificial intelligence to the field of intelligent diagnosis. In order to make better use of artificial intelligence technology to solve the problem of disease intelligent diagnosis with uncertain natural language information, based on the previous work [15,16,17,18], this paper continues to study the linguistic truth-valued concept lattice (LTV-CL) and applies it to medical intelligent diagnosis. In the construction of case base, compared with model A, the construction of concept lattice is greatly decreased in quantity, and each node can be used as a necessary reference to directly give the final diagnosis results in the feature matching of patients to be diagnosed. Based on the constructed case base, the attribute matching is carried out from two aspects: extrinsic attribute and intrinsic attribute

Attribute Matching

C20 Figure 3

Extrinsic Attribute Matching

Conclusions