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Objective To present a named entity recognition method referred to as BioBERT-Att-BiLSTM-CRF for eligibility criteria based on the BioBERT pretrained model.The method can automatically extract relevant information from clinical trials and provide assistance in efficiently formulating eligibility criteria.Methods Based on the UMLS medical semantic network and expert-defined rules,the study established medical entity annotation rules and constructed a named entity recognition corpus to clarify the entity recognition task.BioBERT-Att-BiLSTM-CRF converted the text into BioBERT vectors and inputted them into a bidirectional long short-term memory network to capture contextual semantic features.Meanwhile,attention mechanisms were applied to extract key features,and a conditional random field was used for decoding and outputting the optimal label sequence.Results BioBERT-Att-BiLSTM-CRF outperformed other baseline models on the eligibility criteria named entity recognition dataset.Conclusion BioBERT-Att-BiLSTM-CRF can efficiently extract eligibility criteria-related information from clinical trials,thereby enhancing the scientific validity of clinical trial registration data and providing assistance in the formulation of eligibility criteria for clinical trials.

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With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.

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Objective To discuss the specific role of association rules in the compatibility of herbs and the relationship between herbs and symptoms by analyzing ancient analogous prescriptions Zhizichi Decoction with SQL Server software.Methods The prescriptions which contain Gardenia and Semen Sojae Preparatum were searched in the database of “Ancient Prescriptions for Epidemic Disease”, and they were imported to software with the standardized data of their symptoms and ingredients, then the proper association rules were found. The laws between parameter setting and rule generation were searched through parameter adjustment.Results Many laws of the drug compatibility and relationship between herbs and symptoms were obtained effectively in data mining by adjusting the parameters:The support setting was good for finding common used drug compatibility, and the confidence was good for finding specific drug compatibility and relationship between herbs and symptoms;the item set which had high frequency, sometimes made the rule’s importance low.Conclusion The laws of compatibility of herbs and the relationship between herbs and symptoms for data mining of analogous prescriptions are discovered by analyzing the support, confidence and importance of the association-rules and the clinical magnificence between symptoms and herbs.