智能医疗信息系统下患者健康管理机制的优化

Lifang Zheng, Weixia Liu, Hangying Chen
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引用次数: 0

摘要

建立科学完整的智能医疗信息分析应用模型,对推动智能医疗信息的应用具有重要意义。针对人工鱼群算法(AFSA)迭代收敛速度快、优化精度低以及粒子群算法(PSO)易陷入局部极值的不足,将人工鱼群算法(AFSA)与粒子群算法(PSO)相结合。利用粒子群算法快速的局部收敛能力,克服了AFSA算法求解精度低、收敛速度慢的缺点。在分类阶段,我们尝试应用机器学习技术对标记的特征向量进行分类,评估和分析这两种机器学习算法在智能医疗诊断辅助应用中的性能,并使用当今流行的深度学习分类方法(即智能优化文本分类模型)和机器学习分类方法对分类效果进行比较。评价和分析了分类模型在智能医疗诊断辅助应用中的适用性。实验结果表明,将机器学习方法应用于疾病类型判断的准确率达到90%以上,完全符合患者对疾病的判断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System
The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.
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