Interactive crowdsourcing to spontaneous reporting of Adverse Drug Reactions

• Published in: 2014 IEEE International Conference on Communications (ICC) pp. 4275 - 4280
• Main Authors: Chao Chen, Yining Huang, Yi Liu, Chengdong Liu, Lingchao Meng, Yunchuang Sun, Kaigui Bian, Anpeng Huang, Xiaohui Duan, Bingli Jiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Biological cells; Decision trees; Drugs; Genetic algorithms; Knowledge based systems; Signal detection
• ISSN: 1550-3607; 1938-1883
• DOI: 10.1109/ICC.2014.6883992

Adverse Drug Reactions (ADRs) has become a worldwide problem that draws the attention of people from all racial and ethnic groups. The number of deaths caused by ADRs has greatly increased and led to many drug withdrawals in the last decades. Recent research findings indicate that most ADRs can be effectively prevented to some extent by using computer-aided information technologies. Though many spontaneous reporting systems (SRSs) have been built to enhance the pharma-covigilance, the ADRs data is still very sparse because the large amount of reports obtained from consumers contains insufficient hints to identify a possible causal relationship between an adverse event and drug. Based on this motivation, we developed Adverse-Tracking, a spontaneous reporting system of ADRs via crowd-sourcing. Our proposed system interacts with consumers through a Q&A interface and collects the ADR reports. The decision tree support vector machine (DTSVM) based on the genetic algorithm is used in our system to automate the Q&A procedure. We carried out experiments to evaluate the performance at Peking University First Hospital. As demonstrated by the results, our system is an efficient tool to track and discover adverse events in the consumers' reports of ADRs, which facilitates the detection of "signal".