ABSTRACT
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.
ABSTRACT
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.
Subject(s)
Pattern Recognition, Automated , Medical InformaticsABSTRACT
AIM: To evaluate the clinical efficacy and safety of He-wei-zhi-xie (HWZX) capsules in diarrhea patients. METHODS: The clinical study was conducted in 35 clinical trials centers from October 2015 to December 2017 by multicenter, prospective, open and uncontrolled design methods. The primary efficacy endpoint is the effective rate of diarrhea, the secondary endpoints include recovery rate of diarrhea, recovery time of diarrhea, number of irregular stools and Leeds dyspepsia questionnaire. The pharmacodynamics model of time course was established by nonlinear mixed effect model, and the effect of covariates on pharmacodynamic parameters was investigated. The safety measures were the incidence of adverse events, adverse reactions and the laboratory test indicators. RESULTS: A total of 2 285 cases were included in full analysis set. The effective rate of diarrhea was 90.8%, and the diarrhea recovery rate was 77.3%. The median time of recovery was 3 days, and the Leeds score was reduced by 3.6 points. It is found that baseline has a significant effect on model parameter E