Adverse drug reactions (ADRs) are increasingly becoming a serious public health problem. Spontaneous reporting systems (SRSs) are an important way for many countries to monitor ADRs produced in the clinical use of drugs, and they are the main data source for ADR signal detection. The traditional signal detection methods are based on disproportionality analysis (DPA) and lack the application of data mining technology. In this paper, we selected the spontaneous reports from 2011 to 2018 in Jiangsu Province of China as the research data and used association rules analysis (ARA) to mine signals. We defined some important metrics of the ARA according to the two-dimensional contingency table of ADRs, such as Confidence and Lift, and constructed performance evaluation indicators such as Precision, Recall, and F1 as objective standards. We used experimental methods based on data to objectively determine the optimal thresholds of the corresponding metrics, which, in the best case, are Confidence = 0.007 and Lift = 1. We obtained the average performance of the method through 10-fold cross-validation. The experimental results showed that F1 increased from 31.43% in the MHRA method to 40.38% in the ARA method; this was a significant improvement. To reduce drug risk and provide decision making for drug safety, more data mining methods need to be introduced and applied to ADR signal detection.

Introduction: Adverse drug reactions (ADR) are directly related to public health and become the focus of public and media attention. At present, a large number of ADR events have been reported on the Internet, but the mining and utilization of such information resources is insufficient. Named entity recognition (NER) is the basic work of many natural language processing (NLP) tasks, which aims to identify entities with specific meanings from natural language texts.Methods: In order to identify entities from ADR event data resources more effectively, so as to provide valuable health knowledge for people, this paper introduces ALBERT in the input presentation layer on the basis of the classic BiLSTM-CRF model, and proposes a method of ADR named entity recognition based on the ALBERT-BiLSTM-CRF model. The textual information about ADR on the website “Chinese medical information query platform” (https://www.dayi.org.cn) was collected by the crawler and used as research data, and the BIO method was used to label three types of entities: drug name (DRN), drug component (COM), and adverse drug reactions (ADR) to build a corpus. Then, the words were mapped to the word vector by using the ALBERT module to obtain the character level semantic information, the context coding was performed by the BiLSTM module, and the label decoding was using the CRF module to predict the real label.Results: Based on the constructed corpus, experimental comparisons were made with two classical models, namely, BiLSTM-CRF and BERT-BiLSTM-CRF. The experimental results show that the F1 of our method is 91.19% on the whole, which is 1.5% and 1.37% higher than the other two models respectively, and the performance of recognition of three types of entities is significantly improved, which proves the superiority of this method.Discussion: The method proposed can be used effectively in NER from ADR information on the Internet, which provides a basis for the extraction of drug-related entity relationships and the construction of knowledge graph, thus playing a role in practical health systems such as intelligent diagnosis, risk reasoning and automatic question answering.

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