Disease Prediction and Early Intervention System Based on Symptom Similarity Analysis

Abstract

With the development of computer technology, the electronic of medical data has become a reality. Now, how to analyze the data sufficiently to predict patient's disease and conduct early intervention has become a focused research direction. The patient's intuitive expression of feelings is also an aspect that cannot be ignored. Doctors record the pathological characteristics of patients in system. In the paper, we proposed a sentence similarity model to carry out symptom similarity analysis to achieve elementary disease prediction and early intervention, which makes use of word embedding and convolutional neural network (CNN) to extract a sentence vector that contains keyword information about the patient's feelings and symptoms. In order to increase the accuracy of sentence similarity computation, this model integrated syntactic tree and neural network into the computation process. Our main innovation is to use symptom similarity analysis model for disease prediction and early intervention. In addition, the SPO kernel is also one of the innovations. Finally, the results of experiment on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance reached 83.9% in the terms of F1 and accuracy. Furthermore, we also conducted experiments on the data of the Semantic Textual Similarity task. Pearson correlation coefficient indicates that our result is closer to the gold standard scores, which illustrates that it can extract the key information of sentence well to realize the prediction of disease and carry out early intervention.INDEX TERMS Convolutional neural network, disease predictions, early intervention, symptom similarity analysis.

Security Enhanced Sentence Similarity Computing Model Based on Convolutional Neural Network

“…The method mainly takes advantage of syntactic and semantic features to assess the similarity. [50] proposed a more comprehensive model of sentence feature extraction, which collects the semantic information, syntactic information and word order information of sentences at the same time. When extracting syntactic information, the model only extracts the subject, predicate and object of the sentence to construct the sentence matrix, which leads to incomplete feature extraction of the sentence.…”

“…• The sentence similarity analysis model based on convolutional neural network proposed by Zhang et al [50] takes into account the syntactic and semantic information of the sentence at the same time. After extracting the long sentence, the model can pay attention to the main components of the sentence.…”

Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion

“…The weights used in the calculation are obtained from the performance evaluation parameters of the three models, as shown in Formula (9).…”

“…Zhang et al [9] propose a novel text similarity calculation model. In this model, the semantic information and location information of words in the text are taken into account.…”

Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion

“…Researchers apply attention mechanism to NLP tasks, hoping that the model can focus on text feature extraction in a more holistic way. Zhang et al [10] propose a novel text similarity calculation model. In this model, the semantic information and location information of words in the text are taken into account.…”