In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional knowledge.An automatic and accurate tentative...In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional knowledge.An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical resources.Despite its great value,little work has been conducted on this diagnosis method.Thus,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical text.The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)network.The symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases.Finally,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code.Model training and evaluation are performed on a public clinical dataset.The results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412.展开更多
基金We thank the anonymous reviewers for their helpful comments.This work was supported in part by the Science and Technology Major Project of Changsha(No.kh2202004)the National Natural Science Foundation of China(No.62006251)。
文摘In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional knowledge.An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical resources.Despite its great value,little work has been conducted on this diagnosis method.Thus,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical text.The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)network.The symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases.Finally,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code.Model training and evaluation are performed on a public clinical dataset.The results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412.
