Expression of Concern: Construction of community health care integration using artificial intelligence models

The level of medical care is improving, and the life expectancy in China is increasing, but at the same time, the new lifestyle and new family model make the fertility rate is also decreasing, which will lead to the imbalance of China's population structure and the aging of the population is becoming more and more serious. The 2017 Social Services Development Statistics Bulletin (Abir et al. 2020) shows that by the end of 2017, China will have 35% of its population over the age of 60 years, making it the most severely aging country in the world (Chen 2019). When China entered the aging society, it was not yet modernized, its economy was underdeveloped, and it had the demographic characteristic of ‘aging before getting rich’ (Alemdar & Ersoy 2010). The dependency ratio of China's elderly population shows a trend of increasing year by year, as shown in Table 1.

In today's growing elderly population and elderly mobile population, the traditional elderly care model is single, and the lack of medical functions and resources in traditional elderly care institutions, and in the process of long-term care and nursing care, care and medical care are closely related, and many disabled elderly people need more comprehensive care in long-term care combined with medical resources (Yuehong et al. 2016; Guo et al. 2019). Single traditional elderly care institutions cannot meet the long-term ‘medical + nursing’ needs of the elderly in a comprehensive manner. This problem can be solved by community-based ‘medical + nursing’ elderly care services, which are government-led, community-based. Community-based ‘combined medical and health care’ elderly care services are an important solution to the problem of socialized elderly care in the context of the increasing population aging in China, and a new way to alleviate the current unreasonable allocation of social elderly and medical resources (Chan et al. 2012).

Community health care integration is to take the community as a platform to fully combine the elderly resources with medical resources, and improve the effectiveness of the combination of elderly and medical resources, which can ensure that the elderly can enjoy cultural activities, daily care and other elderly services through the community on the one hand, and also enable the elderly to directly enjoy preventive health care, rehabilitation treatment and other medical On the other hand, it also enables the elderly to enjoy medical services such as preventive health care and rehabilitation treatment directly in the community (Górriz et al. 2020). Compared with the traditional community elderly care model, the most prominent feature of the community ‘combined medical care’ elderly care service model is that it can provide not only simple preventive medical services for the elderly but also specialized medical services such as health examination, emergency treatment, rehabilitation care and disease treatment for the elderly. The proportion of the community elderly service model is about 6%, and the proportion of the institutional elderly care model is about 4% of all elderly care models (Islam et al. 2015).

Under the vision of artificial intelligence, the community health care information service system uses big data, Internet, and other information technologies to provide intelligent health elderly services in institutions, mainly by providing the elderly with regular and real-time monitoring of physical health data, mobile positioning, alerts of abnormal conditions, and physical health data management (Bardhan et al. 2020). It realizes the information transmission and interaction between the elderly and the elderly institutions, medical caregivers, and children, and provides effective monitoring of the daily activities and health conditions of the elderly in the elderly institutions, as well as provides the elderly with other needs in life and entertainment, and improves the elderly services in the elderly institutions. Traditional data analysis and data transmission methods have difficulties in data query, information sharing, inspection and supervision, paper storage, and data omission, which are unable to effectively analyze and process such a large and complicated data type to provide more help to the upper level (Fritz & Dermody 2019).

The full English name of the CART model is Classification and Regression Tree, and its corresponding Chinese interpretation is Classification Regression Tree. In general, this method is a classification data mining algorithm. It is a flexible way of describing the conditional distribution of the variable Y given the predictive vector value X. The model uses a binary tree to recursively divide the prediction space into subsets where Y is distributed continuously and uniformly. The leaf nodes in the tree correspond to different regions of the division, which is determined by the branch rules associated with each internal node. By moving from root to leaf node, a prediction sample is assigned a unique leaf node, and the conditional distribution of Y on that node is also determined. This method was proposed by Breman et al. There are significant differences between this method and the previous classification method ID3, which are mainly reflected in the following three aspects: the impure measure used to select variables in CART is the Gini index; If the target variable is nominal and has more than two classes, CART may consider merging the target class into two superclasses (bimorphization); If the target variable is continuous, the CART algorithm finds a set of tree-based regression equations to predict the target variable. The core idea of the algorithm is binary recursion, through this binary way, the collection data originally classified in a big class is segmented. That is to say, a sample set is divided into two sample sets and four sample sets in turn. The specific progress of sample segmentation is determined by the specific attributes of the data. By this segmentation, there are two branches in every node of non-leaf node. There are 4 different levels of impurity that can be used to find the partitioning of CART models, depending on the type of target variable, for the target variable of classification, you can choose GINI, bibialization or ordered bialization; For continuous target variables, the least square deviation (LSD) or the least absolute deviation (LAD) can be used. The core of the CART algorithm to maintain efficiency is to ensure that every non-leaf node exists with the maximum probability, which is enough to say, that for the leaf nodes with low or extremely low probability, pruning operations must be carried out to ensure the execution efficiency of CART tree.

In a word, although the informatization of community medical integration is still in its infancy, the current situation of elderly care services can be improved by using advanced information technology, effectively integrating social elderly care service resources and improving management efficiency. This will certainly bring new direction to the elderly care service in the future, and provide better and more efficient services for the united community health care.

With the promotion of artificial intelligence technology, the construction of community health integration has become a reality. With the gradual improvement of community medical information, we can foresee that community elderly care services will usher in a revolutionary change. Through the use of advanced information technology, the social elderly care service resources are effectively integrated to improve management efficiency and service quality, with the guidance of an artificial intelligence model, community health care integration is moving towards a more intelligent and efficient development direction. Through the application of intelligent AI technology and the Internet of Things, community medical care will further improve the quality of service and better meet the needs of residents for the elderly. At the same time, community health care also needs to achieve close cooperation with other related institutions to jointly promote the development of community health integration. It is believed that in the near future, the integration of community health care will bring more high-quality and efficient elderly care services for our elderly.

The population structure of European society has undergone unprecedented changes – the population is rapidly aging. The aging problem has a direct impact on the social and economic development of Europe. In this context, foreign scholars have carried out research and have conducted more in-depth research and exploration on this topic.

First, in terms of community-based elderly care theory, the literature (Azzi et al. 2020) points out that the problem of medical reimbursement in medical and nursing care service institutions is not well solved, which restricts a series of problems brought about by reimbursement, and the lack of corresponding policy support brings about a lag in the development of the institutions. The literature (Gams et al. 2019) points out that from the perspective of the current long-term development of the floating elderly population, all relevant institutions lack systematicness and linkage, and the entire medical and elderly care system is not planned in a comprehensive way. The literature (Holroyd 2022) points out that the ratio of institutional beds to the number of elderly population in elderly care institutions does not meet the standard. There is still some distance from the national target. The development between urban and rural areas and between elderly institutions with different attributes has also not reached a balance. Some elderly institutions have too low occupancy rates due to poor environmental facilities, while others discourage the elderly due to inflated prices.

Second, in terms of medical services, in recent years, more and more elderly people in China have lost their self-care ability and become disabled, and the number of elderly people with full self-care ability is decreasing. The literature (Pramanik et al. 2017) argues that the combination of medical institutions and elderly institutions consists of three joint cooperation models between elderly institutions and medical institutions to achieve the organic combination of both. Literature (Batty et al. 2012; Skouby & Lynggaard 2014; Syed et al. 2019) pointed out that it is necessary to form a government-led force, social forces go hand in hand, and the whole society participates in a senior care service system that highlights the combination of medical care and nursing care. Literature (Zhou et al. 2022) proposed to establish a social insurance long-term care system, fund ‘contracted doctors’, change the payment structure of medical insurance, participate in medical insurance funds to raise combined services, etc., and reasonably design the coverage items and reimbursement scope.

Thirdly, in terms of combined community medical and nursing care services. In developed countries, due to the influence of economic and cultural factors, the population services under the combined medical and nursing model are also faster, and the United States first defined such elderly care services as ‘long-term care system for the elderly’ in 1963. In recent years, the literature (Cai et al. 2019) suggests that the integration of medical and elderly services can have a positive impact on society as a whole, optimizing the service experience of the elderly population while reducing costs and promoting the rational allocation and utilization of resources for the participating social organizations. According to the literature (Sakr & Elgammal 2016), the elderly care services under the integrated medical care model should cover ‘nursing care, non-technical care and professional medical care’, and should meet the special. A study in the literature (Roberts et al. 2021) found that older age groups generally prefer to receive services without being away from their families or communities of origin and that they would feel more secure, identified, and autonomous as a result. In terms of service provision, the literature (Li et al. 2015) argues that, in addition to the support of family members, the power of social organizations should not be neglected in the long-term aging process of the elderly, and that by working together to maximize the value of the community, they are committed.

In fact, the literature (Acampora et al. 2013; Ng 2018) generally argues that the special national situation of population aging and the changing needs of elderly groups in China for elderly care make the establishment of a combined medical and nursing care service model inevitable. Second, consumption upgrading, as a product of economic and technological development, represents that users' consumption behaviors and concepts are changing. This is also true for the elderly market, where people's demand for elderly care is no longer limited to material life, and it is imperative to expand the value space of elderly care services. Accordingly, the literature (Doukas et al. 2011) points out that the spiritual needs of the elderly and the pursuit of health and quality of life are growing, and the traditional elderly service model. In terms of resource allocation and utilization, literature (Doukas et al. 2011) also pointed out that medical and elderly services have long been two relatively independent service modules, for example, elderly people are hospitalized for a long time due to chronic diseases, which causes a waste of resources for the hospitals providing the services, and the combined medical and elderly care model can effectively solve this problem of unbalanced resource allocation.

In summary, the integration of the elderly mobile population based on data analysis and intelligent coordination in the context of artificial intelligence has a wide social value and theoretical foundation.

Through the application of artificial intelligence, a community health integration model can be built to provide comprehensive health management and care services for the elderly floating population. In the context of artificial intelligence, the use of data analysis and intelligent coordination can better solve the social problems faced by the elderly floating population.

To sum up, using data analysis and intelligent coordination under the background of artificial intelligence to build a community health care integration model has broad social value and theoretical basis for the elderly floating population. Through the use of artificial intelligence technology, comprehensive health management and care services for the elderly floating population can be achieved, and their quality of life and happiness can be improved. At the same time, it also provides new directions and opportunities for community building and social development. In the future, we look forward to the AI model further playing a role in the integration of community health care to create a healthier and better life for the elderly migrant population.

As early as 2012 people paid attention to and evaluated the operability and safety of IoT technology in medical elderly services, the information collected through IoT technology has been applied to data analysis on a large scale.

In the construction of the mathematical model of community health care integration for the elderly mobile population, the decision tree algorithm classifies according to the optimal features, and the split features are selected by using the information gain method, and those with large gain values are preferred, but in the classification process, it is easy to have too many feature values leading to too large information gain values, which makes the classification efficiency and accuracy seriously decreased.

Description: Variable p_k represents the prior probability of node data belonging to class k; Variable. The variable n represents the total number of classes; The variable GINI stands for the Gini coefficient. GINI is the impurity function E(t).

Then, based on the overall logic of the above design service and the specific implementation process, the community monitoring environment based on IoT technology is built by using appropriate smart terminal hardware to ensure the personal safety of community elders while laying the foundation for the subsequent implementation of intelligent functions and empirical analysis of the data set.

In addition, mining effective information from the collected data and using it is the key to realize intelligent services, in which data pre-processing is an important part of the data analysis process and an important basis to ensure scientific and accurate research prediction.

This paper refined the data provided by the China Geriatrics Center and obtained 2,311 valid sample data that meet the collection criteria of this paper, including 1,103 male data and 1,208 female data, with an average age of 63.2 years. In this paper, we use these data as samples to build and train the predictive model of typical cases of middle-aged and elderly people in the floating population, such as heart disease. This model does not exclude the wider population, including the elderly in the community. There are blank values, incorrect data values, and inconsistent data formats in the original data set. Therefore, this paper processes data by removing null values, data conversion and data interpolation. The section data intercepted after preprocessing is shown in Table 2, and the feature meanings are shown in Table 3.

In order to improve the accuracy of the results, this paper uses the parameter value of the disease (target) as an indicator, and uses the Kano optimization model to summarize the correlation, so as to calculate the demand importance score of the elderly floating population. If the score is greater than 1, it means that the elderly floating population has a demand for disease care, and if it is 0, it means that the elderly floating population has a small demand for disease care.

Based on the analysis of the data set module, the parameters were run 218 and 250 times in the two surveys, and 214 and 244 valid parameters were recovered for each, with a parameter efficiency of 98.17 and 97.60%, respectively. The general information of the elderly in both groups is shown in Table 6.

Table 6

Comparison table of operating parameters of data sets

Project .	Elderly living in Elderly Care Department (n = 214) .		Elderly at home (n = 244) .		.	P .
	Number of cases (n) .	% .	Number of cases (n) .	% .
Age
60–65	16	7.28	29	11.56	7.403	0.115
66–70	32	15.52	48	18.21
71–75	34	14.52	49	19.56
76–80	34	15.98	36	13.52
80	99	46.58	88	38.52
Gender
Male	75	34.55	105	42.63	3.125	0.078
Female sex	141	65.32	140	52.78

Project .	Elderly living in Elderly Care Department (n = 214) .		Elderly at home (n = 244) .		.	P .
	Number of cases (n) .	% .	Number of cases (n) .	% .
Age
60–65	16	7.28	29	11.56	7.403	0.115
66–70	32	15.52	48	18.21
71–75	34	14.52	49	19.56
76–80	34	15.98	36	13.52
80	99	46.58	88	38.52
Gender
Male	75	34.55	105	42.63	3.125	0.078
Female sex	141	65.32	140	52.78

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Therefore, we can obtain the results of the integrated numerical model of elderly floating population community medical care, as shown in Table 7.

As shown in Figure 10 and Table 9, the AUC value of the ROC curve reached 0.964, which is close to 1, while the significance level of the model has a P value less than 0.05, its KMO value is 0.93, the Bartlett's spherical test reaches a significant level, and there is no multiple loading, and the loading values of each entry on the corresponding factors are 0.478–0.897 indicates that the model classification effect is more satisfactory and has statistical significance.

In summary, the CART decision tree classification algorithm constructed in this paper is implemented by running in a python 3.6 environment and compiled by jupyternotebook, firstly, the existing sample set is randomly partitioned into two parts, respectively, followed by cross-validation folding 10 times to build the model and improve the model robustness, as measured by experimental The accuracy and validity of the model were improved by 7% compared with the traditional model.

From the analysis results, the Stacking algorithm with the combination of multiple strong learners has significantly improved the accuracy of predicting the morbidity of the elderly mobile population in the community compared with other integrated algorithms and single algorithms, indicating that the Stacking algorithm can effectively combine the advantages of multiple strong learners, and the method of obtaining the predicted values through cross-validation can enhance the classification while avoiding overfitting. In terms of sensitivity and specificity and Jorden's index, the values of sensitivity and specificity of the Stacking algorithm are higher and similar compared with other algorithms, indicating that the prediction ability of onset of illness and non-illness of mobile elders in the community is stronger and more stable, and the classification effect is the best, and the learning ability of optimized single classifier is the best, which can enhance the match between predicted and actual classification and maximize the improve the classification performance.

In recent years, China's aging population has faced increasingly severe challenges due to rapid economic and social development, as well as the combined impact of fertility policies resulting in a significant decline in birth rates and delayed average life expectancy. Consequently, ensuring a high-quality life for the elderly has become an important issue for the Chinese government. In response to this, community medical care services have emerged as an effective means to alleviate pension problems and promote healthy aging. This study aims to construct a monitoring environment based on data analysis and intelligent coordination. It clarifies the utilization of Internet of Things devices and data collection methods, designs an E-R database model, defines a database storage model, calculates the significance of integrating community health care indicators for elderly migrant populations, conducts reliability analysis and empirical validity tests on the digital model of community medical integration for elderly migrants. The experimental results demonstrate that the KMO value of the model is 0.93 with Bartlett's sphericity test reaching statistical significance while avoiding multicollinearity issues. Each item loads between 0.478–0.897 onto its corresponding factor indicating higher accuracy (7% improvement) and effectiveness compared to traditional models. The establishment of this integrated health care-nursing care model provides valuable data references towards building an elder-friendly society in China.

The study was supported by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions, Grant Number:2021QN022.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.