Purpose Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of infectious diseases so as to assist in clinical infectious-disease decision-making. Methods Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious diseases is constructed. The data includes 20,620 cases covering seven types of infectious diseases, including outpatients and inpatients, of which training data accounted for 80%, i.e., 16,496 cases, and test data accounted for 20%, i.e., 4124 cases. Through the auto-encoder, data normalization and sparse data densification processing are carried out to improve the model training effect. A residual network and attention mechanism are introduced into the MIDDM model to improve the performance of the model. Result MIDDM achieved improved prediction results in diagnosing seven kinds of infectious diseases. In the case of similar disease diagnosis characteristics and similar interference factors, the prediction accuracy of disease classification with more sample data is significantly higher than the prediction accuracy of disease classification with fewer sample data. For instance, the training data for viral hepatitis, influenza, and hand foot and mouth disease were 2954, 3924, and 3015 respectively and the corresponding test accuracy rates were 99.86%, 98.47%, and 97.31%. There is less training data for syphilis, infectious diarrhea, and measles, i.e., 1208, 575, and 190 respectively and the corresponding test accuracy rates were noticeably lower, i.e., 83.03%, 87.30%, and42.11%. We also compared the MIDDM model with the models used in other studies. Using the same input data, taking viral hepatitis as an example, the accuracy of MIDDM is 99.44%, which is significantly higher than that of XGBoost (96.19%), Decision tree (90.13%), Bayesian method (85.19%), and logistic regression (91.26%). Other diseases were also significantly better predicted by MIDDM than by these three models. Conclusion The application of the MIDDM model to multi-class diagnosis and prediction of infectious diseases can improve the accuracy of infectious-disease diagnosis. However, these results need to be further confirmed via clinical randomized controlled trials.
To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.
BACKGROUND This study focuses on analyzing real data from a hospital to provide timely warnings of known infectious diseases with a view to actively preventing epidemics. OBJECTIVE The aim is to design MSRD model to predict the epidemic trend of infectious diseases based on real hospital data. METHODS Based on the daily reported data of infectious diseases between 2012–2020 from a large Chinese hospital, we selected seven common infectious diseases and constructed a Multi Self-regression Deep (MSRD) neural network model. This model, which is based on a recurrent neural network, can effectively model the epidemic trend of infectious diseases while considering the current influential factors and characteristics of historical development when calculating time-series data. The mean absolute error (MAE) and the root mean square error (RMSE) are used to evaluate the model’s fit and prediction accuracy. RESULTS We compared the MSRD model proposed in this study with the infectious disease SEIR-model using the national public health dataset on COVID-19 and another in-hospital infectious disease, namely, Hand-Foot-and-Mouth disease (HFMD). In an experiment with the national public health dataset, the MSRD proposed in this study demonstrated better performance than the SEIR model, which is because of the SEIR model being limited by factors such as the latent population. The SEIR model is hard to apply to real-world hospital scenarios. Our MSRD model is compared with other neural network methods. The dataset is from real hospital medical records for January 2012–December 2020. The MAE of the MSRD neural network for HFMD and influenza was as low as 0.6928 and 1.3782, respectively. In addition, our MSRD model was compared against other neural network methods such as SVM, Lasso, and Bayes; the MAE and RMSE were both better than those of other neural networks. CONCLUSIONS Our MSRD neural network has high prediction accuracy and can predict the development trend of infectious diseases on a daily basis. The MSRD model can act as a hospital infectious-disease early-warning system.
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