Collaborative interactive service recommendation method for long-term old-age care

Abstract

The invention discloses a collaborative interactive service recommendation method for long-term old-age care. According to the method, for the ubiquitous defects of old-age care historical data in quality and quantity, intelligent algorithm-driven collaborative interactive service recommendation is carried out through three key links of training data expansion and coding, model training and testing based on a random forest algorithm and manual iterative optimization of a model recommendation scheme. The method is designed on the basis of the current situation of real elderly care data, variousproblems of existing elderly care data can be well solved, the actual effectiveness and usability of an elderly care generation scheme are improved, then the huge elderly personalized care requirement is intelligently and efficiently met, and the stress of tense care resources is relieved.

Description

technical field [0001] The invention relates to the field of data-driven intelligent elderly care services, and is an intelligent service recommendation method for long-term health care of the elderly, that is, a collaborative interactive service recommendation method for long-term elderly care. Background technique [0002] Population aging has become a normal phenomenon in today's society. In response to the ever-increasing aging problem, various elderly care service models have been proposed one after another, providing personalized long-term care services and life support for the elderly who have partially or completely lost their ability to take care of themselves, so as to improve the quality of life of the elderly and help them better. live independently. Long-term care services and life support for the elderly need to provide them with appropriate, continuous and personalized short-term or Long-term health care services to improve the elderly's ability to take care...

AI Technical Summary

Problems solved by technology

On the one hand, due to various reasons such as institutional boundaries and data privacy, the current elderly care data often have problems such as small data quantity, low quality, and large data deviation, which makes it difficult for the existing data status to meet the needs of existing intelligent models for data. The demand for the training set and the small amount of data also make it impossible to eliminate more "abnormal" data with practical significance to improve the accuracy of the prediction system; The process of formulating a care plan is often completed by multiple roles, which makes the algorithm that relies entirely on data and intelligence face the challenge of insufficient "human temperature". The actual effectiveness of "not so good"

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