Research and Application of Drug Pair Extraction Method Integrating Co-occurrence and Semantic Information
Objective To propose a drug pair extraction algorithm integrating co-occurrence and semantic information for prescription data.Methods The prescription data were transformed into matrix data,and the association information between drugs was calculated as the initial screening index.Then the word vector was constructed based on the prescription data,and the semantic similarity between drugs was calculated as the second screening index,so as to extract potential drug pairs.The algorithm of this paper and the classical Apriori algorithm were experimented on 1090 lung cancer outpatient prescriptions respectively,and the experimental extraction results were compared and analyzed,so as to verify the usability and effectiveness of this drug pair extraction algorithm.Results Compared with the Apriori algorithm,the present algorithm had better effect in extracting drug pairs,which could reasonably help to narrow down the range of options of potential drug pairs under the situation of large difference in drug frequencies,and successfully extracted 88 groups of drug pairs in medical cases under the range of recommended threshold settings.Conclusion The method of word frequency combined with semantic information for extracting potential drug pairs is feasible and effective,and can provide methodological reference for experience mining in clinical prescription medication.
Drug pair screeningDrug co-occurrenceSemantic informationWord vectorData mining
NETL
NSTL
万方数据
