Intelligent Pre-diagnosis Method and Experimental Research Based on Patient's Personal Information
Objective: Through the analysis of a large number of clinical data, and then explore the intrinsic relationship between symptoms, diagnosis
and treatment records and diseases, it can construct a discriminating system for certain key diseases, which will be very beneficial to the
doctor's diagnosis and treatment or the patient's self pre-diagnosis.
Methods: To solve the problem of pre-diagnosis of the patient department, this problem was explored and tried using the rule-based method.
Based on the results of the exploration, the classification algorithm of support vector machine and convolutional neural network was
used to complete the multi-classification of the department. In the data preprocessing stage, it mainly introduces how to build a relational
database between medical entities; how to standardize patient departments. For the purpose of effectively utilizing the patient's course
record information to achieve accurate diagnosis and treatment, it is proved through experiments that what kind of diagnosis and treatment
information is used is conducive to improving the correct rate of pre-diagnosis of which type of department.
Conclusion: By comparing the experimental results with the evaluation results of the system, it can be seen that the intelligent pre-diagnosis
method based on the patient's personal information has significantly improved performance compared with the pure rule and support vector