Research on predicting elderly health care needs and personalized service management based on big data and artificial intelligence models

With the increasing trend of global population aging, the traditional healthcare service system is facing enormous challenges. This article aims to construct a framework for predicting elderly healthcare needs and intelligent service management based on big data and artificial intelligence technology. Firstly, we integrated multiple heterogeneous data sources (including health monitoring data, social behavior data, environmental data, and historical service records) to construct a comprehensive system of elderly health care demand characteristics. On this basis, a hybrid prediction model (LSTM XGBoost) combining time series analysis (LSTM) and gradient boosting tree (such as XGBoost) is proposed to accurately predict short-term and medium to long-term health care needs at the individual and regional levels. Furthermore, we have designed a multi-agent service management system that dynamically and efficiently allocates and schedules healthcare resources based on predicted results. Through the simulation experiment on the real data of a city's health care service platform for two years, the results show that: 1) the mixed prediction model proposed in this paper is better than the single model in the accuracy of demand prediction; 2) The AI service management system can shorten service response time by an average of 35% and improve resource utilization by 28%. This study provides a feasible technical path and management paradigm for building an intelligent, precise, and forward-looking modern health and wellness service system.