Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records

Abstract

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.

[Retracted] Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning

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Reconciling art and science in the era of personalised medicine: the legacy of Georges Canguilhem

“…In his essay on “The question Concerning Technology” [ 16 ], Heidegger connects techne to the revelation of something in the realm of reality. Again, the idea of contingency is strictly connected to the manufacture ( poiesis ) but entails the responsibility of making it happen or causing [ 16 , 22 , 31 ]. Heidegger goes back to the Greek etimology for cause, aitia, which means “to make present”, “to occasion” in the sense of bringing something that was not present before into time and space.…”

Computational methods applied to syphilis: where are we, and where are we going?

“…Other computational approaches include models based on Deep Learning ( 43 , 49 , 55 ), Convolutional Neural Network Ensemble ( 51 ), Decision Tree ( 47 ), RF ( 31 , 49 , 50 ), Gradient Boosting Machine ( 49 ), Extreme Gradient Boosting (XGBoost) ( 47 – 49 ), Autoregressive Integrated Moving Average (ARIMA) ( 33 , 38 , 41 ), ARIMA with Explanatory Variable ( 38 ), Decomposition ( 38 ), Generalized Estimating Equations ( 36 ), NLP ( 37 , 45 ), Ontology ( 42 ), Complex Networks ( 46 ), Knowledge Embedding Representation ( 40 ), Sexual Infections as Large-Scale Agent-based Simulation model ( 44 ), and Gray Model ( 39 ). Table 4 shows the techniques that obtained the best performances in each study and their respective values according to the metric used for evaluation.…”

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