利用人工智能进行情感分析，以有效管理智慧城市环境中糖尿病患者的健康危机
利用人工智能进行情感分析，以有效管理智慧城市环境中糖尿病患者的健康危机
Sentiment Analysis Utilizing Artificial Intelligence for Effe ctive Health Crisis Management in Diabetics in Smart Urban Environments
——《应用科学与技术趋势杂志》第7卷，第1期，2026年——
【摘要】利用人工智能进行情感分析为智慧城市环境中糖尿病患者的健康危机管理提供了一种变革性的方法。本研究提出了一种基于人工智能的实用解决方案，该方案可集成到现有的智慧城市基础设施中，以支持对糖尿病患者的实时健康危机干预。利用人工智能进行糖尿病患者健康危机管理的情感分析面临的挑战包括：需要高质量、多样化的数据来准确捕捉情感，以及在智慧城市环境中处理敏感健康信息可能存在的隐私问题。本研究旨在利用人工智能进行情感分析，以增强智慧城市环境中糖尿病患者的健康危机管理。由于文本数据来源通常包含无关信息、垃圾信息和异常值等噪声，因此在预处理阶段采用自适应中值滤波技术（AMFT）来降低情感分析中的噪声。AMFT用于降噪，循环神经网络（RNN）用于时间情感分析，以及人工智能驱动的优化相结合，为健康危机预测系统引入了一种新颖且技术先进的方法。循环神经网络 (RNN) 模型因其能够处理序列数据并捕捉时间依赖性，在情感分析方面表现出色，尤其是在智慧城市环境中糖尿病患者的健康危机管理方面。人工智能驱动优化 (AIDO) 可以自动调整 RNN 中情感分析模型的超参数，从而提升模型性能，确保其准确性和高效性。该人工智能驱动的情感分析系统优于传统的监测方法，例如基于规则的词典和基于关键词频率的方法（使用Python实现），其准确率达到0.92，精确率达到 0.90，召回率达到 0.93。该系统体现了对应用科学和技术创新的重视，展示了一个可扩展的智能健康监测框架，可部署于智慧城市和城市卫生系统中。未来，利用人工智能进行情感分析的进一步发展，有望通过整合更多样化的数据源和自适应学习算法，增强对糖尿病患者健康危机的实时监测和预测。
【关键词】情感分析、糖尿病患者、自适应中值滤波、人工智能驱动的优化、智慧城市环境、危机管理
[Abstract] Sentiment analysis utilizing artificial intelligence offers a transformative approach to managing health crises among diabetics in smart urban environments. This research proposes a practical AI-based solution that can be integrated into existing smart urban infrastructure to support real-time health crisis interventions for diabetic patients. Challenges in sentiment analysis for health crisis management in diabetics using AI include the need for high-quality, diverse data to accurately capture sentiment and the potential for privacy issues with sensitive health information in smart urban environments. The objective of this study is to leverage sentiment analysis utilizing artificial intelligence to enhance health crisis management for diabetics within smart urban environments. Adaptive Median Filtering Technique (AMFT) is used in pre-processing to reduce noise in sentiment analysis, as textual data from sources often contains noise such as irrelevant information, spam, and outliers. The combination of AMFT for noise reduction, RNNs for temporal sentiment analysis, and AI-driven optimization introduces a novel, technologically advanced approach to health crisis prediction systems. Recurrent Neural Network (RNN) models are highly effective for sentiment analysis, especially in the health crisis management of diabetics within smart urban environments, due to their ability to process sequential data and capture temporal dependencies. AI-driven optimization (AIDO) can automatically tune hyperparameters of sentiment analysis models in RNNs to improve performance, ensuring the models are both accurate and efficient. The AI-driven sentiment analysis system outperforms traditional monitoring methods, such as rule-based lexicons and keyword frequency-based approaches implemented in Python, achieving an accuracy of 0.92, a precision of 0.90, and a recall of 0.93.The proposed system reflects the focus on applied science and technological innovations by demonstrating a scalable, intelligent health monitoring framework that can be deployed in smart cities and urban health systems. Future advancements in sentiment analysis using artificial intelligence could enhance real-time monitoring and prediction of health crises in diabetics, integrating more diverse data sources and adaptive learning algorithms.
[Key words] Sentiment Analysis, Diabetics, Adaptive Median Filtering, AI-driven optimization, Smart Urban Environments, Crisis Management
论文原文：BH Krishna Mohan, Mong-Fong Horng, Siva Shankar S, Chun-Chih Lo (2026). Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Diabetics in Smart Urban Environments. Journal of Applied Science and Technology Trends, Volume 7, Issue 1, Pages: 01-15. January 2026.
https://doi.org/10.38094/jastt71576
（翻译兼责任编辑：MARY）
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