Study on Data Masking Effect Reduction Method for Adverse Drug Reaction Based on Hybrid Model

Abstract

Abstract: Objective To propose a data masking effect reduction method based on hybrid model to solve the problem of masking in adverse drug reactions (ADRs) signal detection by traditional disproportionality measurement. Methods The model was mixed together with Lasso Logistic Regression(LLR), report removal and IC method. The first round of data removal was carried out by Lasso Logistic Regression method and the second round was to remove the reports which frequency is less than or equal to 4. Then, IC algorithm was used for signal detection. Finally, traditional Chinese medicine data of ADR monitoring reports of China in 2010 was used to conduct experiments, and the Results were tested and verified based on known adverse reaction database and Information Bulletin of National ADR Center. Results The experimental Results showed that this model had better performances in recall rate, precision and F index by comparison with each single method. Conclusion The hybrid model not only improves the accuracy of the ADR signal detection, but also has the ability to detect more true positive signals at early stage. This research may provide a more reliable method for ADR signal detection.

Key words: data masking effect, hybrid model, Lasso Logistic Regression, signal detection

CLC Number:

R994.11

WEI Jian-xiang, ZHANG Jian-yin, LIU Mei-han, LI Ming, SUN Jun, XU Hou-ming. Study on Data Masking Effect Reduction Method for Adverse Drug Reaction Based on Hybrid Model[J]. Chinese Journal of Pharmacovigilance, 2018, 15(6): 343-347.

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