[Purpose/significance] The emergence of social media provides a new way to collect healthcare data. By using natural language management technology,the adverse drug reaction(ADR)signal can be extracted from social media,it has great potential to improve the clinical and scientific knowledge of ADR monitoring.However, the extraction of ADR from patients' reports in the social media is still a major challenge. This paper puts forwards an adverse drug reaction signal extraction framework based on advanced natural language processing techniques.[Method/process] The ADR signal extraction framework include the following implementation steps:Firstly,it recognizes the medical entity from the noisy social media based on multi-dictionary sources matching. Secondly, it applies statistical learning based on the shortest dependency path kernel to extract the adverse drug events.Then, filtering the information on the treatment and application of drugs as well as negative drug adverse events by though the semantic knowledge of the drug safety database. Finally,in order to remove rumors and other noise information, we should categorize the source of the report.[Result/conclusion] We collect data from BBS diabetes to identify the validity of the model,the result shows that each part of the model contributes to its overall performance.
Wei Wei
,
Zheng Du
. The Study of Adverse Drug Reaction Signal Extraction Framework Based on the Integrated Statistical Learning and Semantic Filter[J]. Library and Information Service, 2018
, 62(5)
: 115
-124
.
DOI: 10.13266/j.issn.0252-3116.2018.05.013
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