1. INTRODUCTION

Diabetes is a prevalent metabolic disease that reduces life expectancy. The prevalence of diabetes is projected to increase from 4% in 1995 to 4.5% in 2035, and by 2045, the number of people suffering from diabetes is estimated to reach 629 million worldwide, according to the World Health Organization (WHO) [1,2]. Type 2 diabetes mellitus (T2DM), which accounts for more than 90% of diabetes cases, is one of the four priority noncommunicable diseases identified by the WHO [3]. The global prevalence of T2DM is rapidly increasing due to the aging of the population, urbanization, and changes associated with lifestyle [4]. The significant increase in the prevalence of T2DM has imposed high costs on the individual’s health and substantial challenges to national healthcare systems.

T2DM is mainly caused by insulin resistance (insulin secretion is high but the utilization rate is low) and insufficient insulin secretion, which will lead to increased levels of glycosylated hemoglobin (HbA1c). A high HbA1c level not only leads to the deterioration of T2DM but also leads to many fatal health complications, such as kidney failure, blindness, heart disease, etc. [2]. HbA1c is the most valuable index to judge the state of blood sugar control and the progression of T2DM. The detection of HbA1c is simple and easy, and the results are stable and are not affected by meal times and short-term lifestyle changes. Therefore, in the actual clinical decision-making process, medical workers often use HbA1c as the key criterion for assessing the clinical progression of T2DM. They closely monitor the level of HbA1c in patients with T2DM and adjust the treatment plan accordingly to provide the appropriate clinical intervention that will stabilize its levels near the optimal value. In addition, in 2009, the International Expert Committee recommended the use of HbA1c to diagnose T2DM and prediabetes [2]. In 2010, the American Diabetes Association (ADA) endorsed the recommendations of the Expert Committee on the diagnosis of T2DM and recommended HbA1c as the preferred method [5–7]. In 2011, the WHO approved HbA1c as the gold standard for the diagnosis of T2DM [5,6]. In summary, maintaining HbA1c levels is very important for reducing the risk of T2DM and clinical decision-making.

Therefore, many researchers have extensively analyzed HbA1c. For example, Chemlal et al. used patients’ previous HbA1c levels and physical activity as input variables for the risk prediction model for T2DM [8]. Based on this prediction model, Chemlal et al. also implemented a mobile phone application, which predicts and provides feedback on HbA1c levels in patients with T2DM [9]. Torregrosa et al. developed the 3 point-of-care HbA1c analyzer devices for use in physicians’ offices to provide immediate results and reduce inconvenience to the patients [10]. Georga et al. designed a diabetes monitoring and management system through which patients record details about their daily lives, and obtain HbA1c levels and various alarms according to these records [11]. However, most of these studies have focused on the accuracy of HbA1c monitoring or explored the relationship among glycosylated hemoglobin levels and T2DM and its complications. Few studies have examined the future prediction of HbA1c levels and the role of predicted values in the self-management of patients with T2DM. In addition, the results of these studies remain unclear. Therefore, a challenging research issue is to predict the development of trends in HbA1c levels. The HbA1c levels of patients with T2DM changes with time. In practice, HbA1c is regularly checked every three months in patients with T2DM to reflect their blood glucose control in the past 8–12 weeks. This is obviously a typical time sequential prediction problem. In the current deep learning methods of time sequential prediction, the long short-term memory (LSTM) neural network has been proved to have better performance (described in the literature review), so this paper uses LSTM to predict the trend of HbA1c level. In addition, although LSTM has the ability to capture long-term information, the single use of LSTM to capture long-term information is not effective. Because the gap between HbA1c data in patients with T2DM is long. Here, this paper introduces self-attention (SA) mechanism to enhance the ability of LSTM to capture long-term information, so as to more accurately apply it to the prediction of HbA1c data. In the present study, we aim to accurately predict the future HbA1c levels based only on previous HbA1c levels recorded for patients to ensure that if patients will be able to receive data about a future HbA1c level in advance and only rely on a simple indicator, namely HbA1c records, they will be able to simply and easily manage their T2DM.

The structure of this article is described below. In Section 2, we briefly review the common models that have been applied to predict sequential events. In Section 3, we clarify data acquisition and processing methods. In Section 4, we provide detailed descriptions of the principles and frameworks of the long short-term memory with self-attention mechanism (SA-LSTM) model. Section 5 describes the experimental process and analyzes the results, Section 6 discusses the experimental results, and Section 7 contains the conclusions.

2. LITERRATURE REVIEW

The future development of T2DM mainly depends on the patients’ conditions and interventions received over a long period, and the prediction of T2DM based on the HbA1c level is a sequential predictive event. Additionally, the data of HbA1c levels obtained from EMR used in this paper are time sequential and have the characteristics of continuous and irregular intervals. Therefore, we should use time sequential methods to construct the prediction model, which is consistent with the dynamic development of T2DM. The existing time sequential prediction models in the field of medical health management are described below.

Many researchers have used time discretization data to construct time sequential prediction models, among which the classical models are continuous-time Markov chain models and time Bayesian networks. Bueno et al. used hidden Markov models that are able to cope with uncertainty and sequential phenomena to propose a probabilistic framework for predicting disease dynamics guided by latent states [12]. Nazari et al. suggested that the implementation of a Markov model with a continuous-time process would yield better results for modeling diabetes. By considering the time intervals between the changes in the disease states, the model would provide a more comprehensive perspective of the processes involved in the disease [13]. However, the time interval of each admission is irregular, leading to the irregular intervals of recorded data. In addition, the information recorded at each admission includes continuous data, such as biochemical parameters, discrete variables, and medication data. Continuous-time Markov chain models are often unable to adequately address these complex data. Therefore, Bueno et al. used a dynamic Bayesian network (DBN) model to capture changes in the patient status distribution and explore potential pathophysiological changes over time [14]. Liu et al. noted that typical time Bayesian networks are divided into dynamic Bayesian networks with an assumption of discrete time and continuous-time Bayesian networks with an assumption of continuous time when studying disease processes and compared the advantages of the two networks in different data distributions [15]. Although this approach is advantageous for analyzing complex medical data, its power has yet to be confirmed, and it further assumes that discrete states are inefficient [3,16].

As new application of deep learning methods in the medical health management field are becoming increasingly successful, particularly in medical image and sound data, deep learning methods have attracted increasing attention, among which LSTM is the most advanced method [17,18]. Reddy and Delen used a recurrent neural network (RNN)-LSTM method to predict hospital readmissions for patients with lupus [19]. Tsiouris et al. introduced LSTM networks to predict epileptic seizures based on EEG signals and verified that the proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks [20]. Swapna et al. employed deep learning networks with a convolutional neural network—LSTM to automatically detect the abnormality of heart rate variability signals [17]. Most of these studies used LSTM to generate relevant predictions with time sequential EMR data, which are critical for understanding and slowing disease progression. LSTM has a strong adaptability in time sequential data analysis and performs better in sequence learning than other traditional predicting methods, such as RNNs, support vector machines, hidden Markov models, etc.

However, for long-term interdependence, LSTM must incorporate several time steps of information accumulation to link the data. A longer time results in a lower the effectiveness of capturing information is [21]. Therefore, we introduce the SA mechanism, an efficient deep learning method, to address the shortcomings of LSTM. The SA mechanism ensures that the memory of LSTM is more profound and effective by adequately capturing long-term information interdependence to improve the accuracy of the prediction. In fact, the SA mechanism was first applied in image processing field. With the continuous development of the SA mechanism, it has become a research hotspot in neural networks and has been used in combination with other deep learning methods in different fields and has shown good performance. Wu et al. integrated the SA mechanism into a convolutional neural network to improve image fidelity for accelerated magnetic resonance image acquisition [22]. Vuddagiri et al. constructed deep neural networks with attention architectures (DNN-WA) to develop automatic language identification systems, and the proposed model performed better in terms of an equal error rate [23]. Cinar et al. extended the attention mechanism into RNNs for time sequential forecasting with missing values [24]. However, the SA mechanism is rarely used in the medical health management field or in combination with LSTM. The SA mechanism better solves the problem of long-term interdependence of LSTM in theory. The integration of LSTM and the SA mechanism in the medical health management field requires further study.

In summary, we construct the SA-LSTM model to predict patients’ HbA1c levels using time sequential data in this paper. We compare this improved model with the traditional LSTM and random forest (RF) models to study the effectiveness of the integrated SA-LSTM model in time sequential predictions.

3. DATA PREPROCESSING

4. MODEL

4.1. Self-ASA Mechanism

The attention mechanism was first proposed in the field of visual images. In recent years, the attention mechanism has been widely used in natural language processing based on deep learning methods and achieved good results. Its core idea is to draw lessons from the fact that the human brain will focus on a particular place at a particular time and allocate less attention to unimportant information. The SA mechanism is a special variant of the attention mechanism. More specifically, the attention mechanism occurs between an encoder and decoder, or between input and output information. The SA mechanism occurs within the information sequence and captures the connections between the distant information, which is also called internal attention.

The principle of the SA mechanism is understood from the perspective of key value query. As shown in Figure 1, the key value query has three basic elements: Query, Key, and Value. Supposing we have n key vectors K=k1,…,kn∈Rn×K, each one representing as a vector of dimensionality K. For a given query vector Q∈RK, the system finds the same key and returns the corresponding values V=v1,…,vn∈Rn×V. If the system cannot find the same key, the similarity w of query and each existing key is calculated separately. Notably, w is assigned to all values as a weight, and their weighted sum is returned. Corresponding to the sequential data, the output sequence is the Query, the input sequence is the Key, w represents the similarity of the two, and the Value is set to 1.

Figure 1

The principle diagram of the self-attention mechanism.

The computational steps of the SA mechanism are described below.

1. Calculate the similarity of each Query and Key to obtain Si (weight coefficients for each Key corresponding to the Value). The commonly used calculation methods are the point product method (formula 1), cosine similarity method (formula 2), and neural network (MLP) evaluation method (formula 3).

   SiQ,Ki=Q⋅Ki(1)
   SiQ,Ki=Q⋅Ki‖Q‖⋅‖Ki‖(2)
   SiQ,Ki=MLPQ,Ki(3)

2. By introducing a similar softmax function to normalize the weights, the weights of important elements become more prominent. The calculation is shown in formula 4.

   ai=SoftmaxSi=expSi∑j=1NexpSi(4)

3. The final attention value is obtained by the weighted summation of ai and the corresponding value, and the calculation is shown in formula 5.

   A(Q,S)=∑i=1NaiVi(5)

4.2. Long Short-Term Memory

LSTM is a variant of the RNNs proposed by Hochreiter and Schmidhuber in 1997 [25]. LSTM is suitable for learning from experience and predicting time sequential problems, which is also a difference from other neural network models. The key feature of LSTM to solve time sequential problems is the cell state. LSTM adds and deletes information from the cell state. We must use an information gate, namely, the forgetting gate, input gate and output gate, to achieve this goal. LSTM forgets, retains, and outputs historical information through gate selectivity [26]. The specific structure of LSTM is shown in Figure 2.

Figure 2

The specific structure of long short-term memory (LSTM).

In Figure 2, h denotes the output, x denotes the input, tanh denotes the hyperbolic tangent function, σ denotes the sigmoid function, and A denotes a hidden layer node. As shown in the figure, the gates in LSTM are controlled or activated by a sigmoid function. The output value of the sigmoid function ranges from 0 to 1, which determines how much information is passed through the gate. If the sigmoid function output is 0, the information is not transmitted. If the output is 1, all information is transmitted.

The information processing steps of LSTM are described below.

1. A decision is made about which historical information must flow into the cell through the sigmoid network layer of the forgetting gate. It is expressed using the following formula:

   ft=σWf⋅concatht−1,xt+bf(6)
   where Wf denotes the weight matrix of forgetting gate, bf denotes the bias term, ht−1 denotes the output value of the previous period, and xt denotes the input value of the current period.

2. The new information that must be updated through the input gate is determined. A sigmoid network layer is used to determine the weight of the information that must be updated and a tanh layer is used to calculate the alternative content for updating the current cell. It is expressed using the following formulas:

   Ct~=tanhWC⋅concatht−1,xt+bC(7)
   it=σWi⋅concatht−1,xt+bi(8)
   where WC denotes the weight matrix, bC denotes the bias term, Wi denotes the weight matrix of the input gate, and bi denotes the bias term.

3. The cell state is updated by multiplying the output value of the forgetting gate by the old cell state Ct−1 and adding the product of it and Ct∼ to obtain the new cell state using the following formula:

   Ct=ft⋅Ct−1+it⋅Ct~(9)

4. The information that flows into the next cell from the current cell through the output gate is determined. Similarly, a sigmoid network layer is used to calculate the weight of the output information and then a tanh layer is used to process the information after the current cell is updated. The final output of the current cell is obtained by multiplying the two using the following formula:

   ot=σWo⋅concatht−1,xt+bo(10)
   ht=ot⋅tanhCt(11)
   where Wo denotes the weight matrix of the input gate and bo denotes the bias term.

4.3. Integrated SA-LSTM Model

Based on the introduction to LSTM provided above, it has a good memory of previous information and is a very suitable method for time sequential prediction research. However, as mentioned above, LSTM has a long-term interdependence problem, namely, LSTM calculates hidden states and outputs in a stepwise manner for input sequences, but is unable to easily capture the connections between long-term distant data. It may require more time steps to react. For patients with T2DM, the HbA1c level measured in the hospital undergoes dynamic changes over time. These sequential data are limited and intrinsically correlated, but not an isolated set of points. Therefore, the prediction results obtained using LSTM alone may not be ideal. In this paper, we introduced the SA mechanism into the traditional LSTM method to improve the accuracy and applicability of the prediction. The SA mechanism focuses on sequential information at different times and fully adopts all the contents in front of the sequence to better solve the problem of long-term interdependence [27]. The introduction of the SA mechanism enables the model to store the important intermediate information of the current moment for a subsequent time, which better expands the expressive ability of the neural network and enhances the long-distance interdependence effect.

As shown in Figure 3, the integrated SA-LSTM model consists of four modules: input layer, LSTM layer, SA mechanism layer, and output layer.

Figure 3

The structure of the integrated self-attention long short-term memory (SA-LSTM) model.

1. Input layer: Introduce glycosylated hemoglobin time sequential data from patients with T2DM. The input layer requires three dimensions, namely, sample capacity (batch size), time series (time step), data content (input data). Combined with this experiment, the sample size is the total number of patients with T2DM. Each patient enters the input layer as a sample. Each patient contains a number of sequential glycosylated hemoglobin values to form a time series. The specific glycosylated hemoglobin values in each time series are the data content [26]. See Table A1 of the Appendix for specific sample input values.

2. LSTM layer: There are two LSTM layers. The first LSTM layer includes 35 memory cells and the second LSTM layer, includes 30 memory cells. We introduce Dropout, which is a way to overcome network over-fitting, and adjust the learning ability of the neural network model and learn more robust features adaptively. The dropout rates are set to 0.5.

3. SA mechanism layer: The SA layer uses a multiple linear regression analysis to sense the effect of input data on the predicted value and calculates the weighted value of each feature that affects the predicted value. The SA mechanism achieves a linear combination of all hidden states in LSTM with different weights. Set the input matrix as

   X=X11X12…X1t⋮⋮⋮⋮Xd1Xd2…Xdt

   Then Q and K is calculated as follows:

   Q=ReLUX⋅WQK=ReLUX⋅WK
   where WQ denotes the weight matrices of query, WK denotes the weight matrices of key, ReLU is used as activation function.

4. Output layer: We use regularization to add penalty items to the coefficients trained by the network to reduce the test error. The penalty items punish those coefficients that are too large and increase the coefficients that are too low to prevent the model from over-fitting and obtain the final output. In this experiment, the output layer has only one dimension, that is, the predicted value of glycosylated hemoglobin.

The flow-process of the proposed model is shown in Figure 4. First, we collected the HbA1c test value and test time of T2DM patients. Then, we normalize the preprocessed sequential HbA1c data as the original input data with the formula 12. We train the proposed model with 30% samples to adjust and optimize the parameters. Besides, the feature information of input data is extracted by the LSTM layer and a deep information structure feature is obtained. The SA mechanism operates on the output of LSTM layer using scaled point product attention to learn the structural characteristics of time sequential data and capture long-term interdependence. Finally, we added a regularization term (expressed as the formula 13) to the cost function to reduce the influence of disturbance and noise in training data and obtain the prediction results.

x′t=xi−minmax−min(12)

where xi is the original value, min and max are the minimum and maximum values of the location of the variable xi.

e′=e(w)+λ2SwTw(13)

where e is the original error function, w is the weight vector, λ is set to 0.01 by default.

Figure 4

The flow-process diagram of the integrated self-attention long short-term memory (SA-LSTM) model.

5. EXPERIMENT AND RESULT

5.2. Results

We used RF and LSTM, two commonly used time sequential prediction models, to conduct comparative experiments with the integrated SA-LSTM model. All three models were trained and tested on the same dataset to avoid interference from other factors. The results are shown in the figures below, where the abscissa represents the number of samples in this experiment and the ordinate represents the value of HbA1c. Figure 5 shows the degree of fit of the predicted values and real values from the three models at 1000 sample sizes. Figure 6 shows the degree of fit of the predicted values and real values from the three models at 2000 sample sizes. Figure 7 shows the degree of fit of predicted values and real values from the three models at 3000 sample sizes.

Figure 5

Predicted values from the three models at 1000 sample sizes. (a) The degree of fit of the predicted values and real values obtained by random forest RF. (b) The degree of fit of the predicted values and real values obtained by long short-term memory LSTM. (c) The degree of fit of the predicted values and real values obtained by the integrated self-attention long short-term memory SA-LSTM model.

Figure 6

Predicted values from the three models at 2000 sample sizes. (a) The degree of fit of the predicted values and real values obtained by random forest RF. (b) The degree of fit of the predicted values and real values obtained by long short-term memory LSTM. (c) The degree of fit of the predicted values and real values obtained by the integrated self-attention long short-term memory SA-LSTM model.

Figure 7

Predicted values from the three models at 3000 sample sizes. (a) The degree of fit of the predicted values and real values obtained by random forest RF. (b) The degree of fit of the predicted values and real values obtained by long short-term memory LSTM. (c) The degree of fit of the predicted values and real values obtained by the integrated self-attention long short-term memory SA-LSTM model.

As shown in Figure 5, when the sample size is 1000, the predicted HbA1c values obtained using LSTM and the integrated SA-LSTM model were very similar to the real values, and the results obtained using RF were not ideal compared with the other two models. As shown in Figure 6, when the sample size is 2000, LSTM was more accurate than the RF prediction, and the performance of the integrated SA-LSTM model was obviously better than RF and LSTM. In Figure 7, when the sample size is 3000, the integrated SA-LSTM model provided the best prediction of the HbA1c levels, and an almost perfect match was observed between the predicted values and real values. In particular, the prediction accuracy of the integrated SA-LSTM model was higher with larger sample sizes.

The evaluation results of the integrated SA-LSTM model proposed in this paper, RF and LSTM on 3000 sample datasets are shown in Table 3.

Model	MSE	RMSE	MAE	MAPE
RF	2.6750	1.6355	1.2091	13.8962
LSTM	0.3582	0.5985	0.4126	5.2726
SA-LSTM	0.0230	0.1516	0.1037	1.3764

Table 3

Evaluation results using different criteria.

As shown in the table, when predicting HbA1c levels, the MSE, RMSE, MAE and MAPE of the integrated SA-LSTM model increased by 99.14%, 90.73%, 91.42% and 90.09%, respectively, compared with the RF model. The integrated SA-LSTM model improved the MSE, RMSE, MAE and MAPE by 93.28%, 74.67%, 74.87% and 73.90%, respectively, compared with LSTM. The integrated SA-LSTM model was significantly better than the RF model and showed better predictive performance than LSTM. This finding confirms the feasibility and superiority of the integrated SA-LSTM model, and the introduction of SA mechanism plays an important role in optimization of traditional sequential prediction methods. The SA mechanism solves the problem of long-term interdependence in deep learning methods such as LSTM and improves the accuracy of the time sequential prediction model by increasing the long-distance interdependence of feature elements.

6. DISCUSSION

We have compared the performance of the integrated SA-LSTM model with RF and traditional LSTM models. The performance was evaluated by calculating the MSE, RMSE, MAE, and MAPE. As shown in Table 3, the integrated SA-LSTM model achieved a higher prediction performance than the RF and traditional LSTM models, which are competitive deep learning methods used to generate time sequential predictions. This result may be easily explained.

In this paper, we use regression strategy for the compared RF method. This method refers to the analysis and prediction of samples by building a combination model of multiple decision trees. Each decision tree model will have a prediction value. The regression method will calculate the average value of the prediction value of each tree to obtain the final prediction value. This approach often results in a failure of the RF model to make predictions beyond the range of data in the training set during the regression. When noisy data are present in the dataset, this method is likely to over-fit to reduce the accuracy of the prediction generated by the model. However, the HbA1c levels from patients with T2DM used in this paper will inevitably produce some noisy data due to the long interval and low frequency of data collection. Therefore, the use of RF to predict the HbA1c level in this experiment is not ideal.

LSTM is powerful in analyzing sequential problems because it is able to control the proportion of historical information being remembered through the gate structure and constantly updates the instant information in the new state. In addition, LSTM has a large number of nonlinear transport layers, and its feature expression is more detailed than traditional models; therefore, it can be used in complex modeling environments. When a sufficient number of training samples is available, the LSTM model completely mines the information contained in the dataset without being disturbed by noisy data. However, as mentioned above, LSTM inevitably has the problem of long-term interdependence, namely, when the time at which sequential data were collected is prolonged, the initial information will disappear when time accumulates to a certain value. However, the time interval between patients’ visits to the hospital to detect HbA1c levels is very long. Using the data collected in this paper as an example, the average time interval for detecting HbA1c levels was 184.52 days and the shortest time interval was 28.44 days. Therefore, LSTM is unable to save and transmit information for a long time in this experiment, which reduces the accuracy of the prediction.

Based on these findings, we proposed the integrated SA-LSTM model, which improves the accuracy of the prediction from three aspects by introducing SA mechanism. (i) Capturing long-term interdependence: Attention is computed for the data of time nodes in the SA mechanism; thus, regardless of the distance between the nodes, the maximum path length is only 1, which captures long-term dependencies and improves long-term memory ability. Therefore, the integrated SA-LSTM model improved the MSE, RMSE, MAE, and MAPE by 93.28%, 74.67%, 74.87%, and 73.90%, respectively, compared with LSTM. (ii) Learning the interdependence in the process of sequence transformation: The SA mechanism effectively solves the problem of information loss in different time steps by learning the interdependence relationship in the process of sequential data transformation to improve the accuracy of the prediction generated by the model. However, the RF model is unable to capture the internal relationships of the information and ignores the relationships in the process of sequence transformation. Therefore, the prediction accuracy of the RF model is much lower than the integrated SA-LSTM model for the different measurement indicators, and is reduced by 99.14%, 90.73%, 91.42%, and 90.09%, respectively. (iii) Improving the output quality of the dynamic structure of the model. As shown in Figures 4–6, the degree of fit between the predicted values and the real values obtained from the integrated SA-LSTM model is higher than the RF and the traditional LSTM models, because both the RF and the traditional LSTM models inevitably lose some of the historical information in the prediction process and are unable to distinguish the importance of the retained information. However, the introduction of the SA mechanism enables the model to dynamically acquire some information that requires attention at different times. Therefore, the model more effectively obtains useful information between input data and output data, and then generates a more reasonable output.

7. CONCLUSION

In recent years, the risk of T2DM has consistently increased, which seriously affects people’s quality of life and health. A reduction in the risk of T2DM not only relies on the clinical treatment but also on another very important role, prognosis self-management. The rational implementation of prognosis self-management activities requires patients to have a clear and correct understanding of their current and future situations. Therefore, this paper proposes the integrated SA-LSTM model to predict HbA1c levels, a gold standard indicator that adequately measures the progression of T2DM. The integrated SA-LSTM model introduces the SA mechanism into the hidden layer of the traditional LSTM model, which addresses the limitation of the long-term interdependence of LSTM by utilizing the ability of the SA mechanism to connect distant information. The integrated SA-LSTM model performed better than the RF and traditional LSTM models in predicting HbA1c levels in patients with T2DM.

This paper has three main contributions: (i) SA mechanism solves a persistent problem of LSTM model well and the integrated SA-LSTM model can more accurately predict the sequential trend of HbA1c in patients with T2DM. It provides a good reference for the improvement of deep learning methods in the field of medical and health management. (ii) HbA1c is a key clinical indicator for evaluating the progression of T2DM. Grasping the sequential trend of HbA1c accurately will help patients to better understand their future illness state and adopt a reasonable self-management strategy to reduce the risk of T2DM. (iii) Accurate prediction of HbA1clevel can help medical workers to judge the progression and condition of patients with T2DM, so as to improve the scientific diagnosis and decision-making of medical workers. This is an important basis for rational utilization and optimal allocation of social medical resources. Of course, some limitations in this paper must be acknowledged. First, the prediction of HbA1c levels provides a prognostic self-management strategy for patients only from the perspective of clinical testing information. The prediction model also must incorporate patients’ individual signs and social networks, which have been proven to affect the risk of T2DM, to help patients manage T2DM more comprehensively. Second, most of the patients included in this study were hospitalized five times or more, and the average interval was 184.52 days. If a greater number of hospitalizations occurred at a shorter interval, the sequential data for the HbA1c level would be more conducive to increasing the specific accuracy of the prediction. These shortcomings provide a direction for our future research, such as methods to incorporate patients’ individual characteristics, which are discontinuous and nonsequential data, into the time sequential prediction. Furthermore, we plan to examine whether these features are realized by modifying the initial state of time sequential prediction methods or by acquiring the external input actively at any time step according to the specific event. Moreover, with the emergence of new models of neural networks, the appropriate design of the SA mechanism to better integrate with the new models is also a hot topic to study [16].

In summary, this paper proposed the integrated SA-LSTM model to predict HbA1c levels, which has guiding significance for improving the health management level and reducing the future risk of T2DM. The study further verifies the feasibility of applying deep learning in the field of medical and health management. In the future, we will further study the existing methods and models to improve the adaptability and superiority of deep learning in the field of medical and health management.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

AUTHORS’ CONTRIBUTIONS

Xiaojia Wang and Wenqing Gong contributed to the conception of the study; Xiaojia Wang and Keyu Zhu performed the experiment; Yuxiang Guan and Lushi Yao contributed significantly to analysis and manuscript preparation; Wenqing Gong performed the data analyses and wrote the manuscript; Shanshan Zhang and Weiqun Xu helped perform the analysis with constructive discussions.

ACKNOWLEDGMENTS

This work was supported by a grant from the Key Disease of Diabetes Mellitus Study Center at the National Chinese Medicine Clinical Research Base, the China Scholarship Council, the National Natural Science Foundation of China Grant No. 61876055,71101041, Philosophy and Social Science Cultivation Program of Hefei University of Technology Projects Grant No. JS2019HGXJ0040, JS2019HGXJ0038, National Steering Committee for Graduate Education of Chinese Medicine and Traditional Chinese Medicine Grant No. 20190723-FJ-B39.