Construction of early detection model for acute respiratory infectious diseases based on natural language processing and deep learning
OBJECTIVE To develop an early detection model for acute respiratory infectious diseases using a deep learning algorithm,and to assist in the early identification of respiratory infectious diseases in medical institutions.METHODS Medical records of 6 683 patients with acute respiratory infections from a large tertiary medical institu-tion in Beijing from Jan 2012 to Mar 2023 were collected.We used the bidirectional encoder representations from transformers(BERT)based on natural language processing technology to train word vectors.Combining convolu-tional neural networks(CNN)and bi-directional long short-term memory(BiLSTM),we created an early detec-tion model called BERT_MCB.Its performance was evaluated based on the receiver operating curve,accuracy,re-call,and F1.RESULTS The BERT_MCB model was overall better than the random forest,BERT,BERT_CNN,and BERT_RNN models.The accuracy rate of this model increased by 1.20％-15.80％,precision rate increased by 1.66％-23.69％,recall rate increased by 0.25％-26.75％,and F1 value increased by 0.66％-27.25％.CONCLUSION The early detection model for acute respiratory infectious diseases can accurately identify acute re-spiratory infectious diseases,which showed that deep learning algorithms have promising potential in the early i-dentification of acute respiratory infections.
Acute respiratory infectious diseaseSymptom surveillanceElectronic medical recordDeep learningNatural language processingEarly identification modelInfectious disease surveillance and alarming
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