Signaling adverse drug reactions with novel feature-based similarity model

Adverse drug reactions (ADRs) are a main cause of hospitalization and deaths worldwide. These unanticipated episodes are generally infrequent, but almost all existing ADR signaling techniques are designed to use dataset extracted from spontaneous reporting systems or employed a predefined type of information (e.g., drugs), which suffer from failures to detect unexpected and latent ADRs. In this paper, we propose a novel Feature-based Similarity model (FS) to detect the potential ADRs for medical cases using the electronic patient dataset. FS is tested on the real patient data retrieved from the US Food Drug Administration that includes 54,070 patients detail information and 9,567 ADRs records. Our model ranked all ADRs for the given medical case that combined the information of drugs, medical conditions, and patient profiles and can be applied in therapy decision support systems and unexpected ADR warning systems. The experimental results show that FS outperforms comparing methods. This paper clearly illustrates the great potential along the new direction of ADR signal generate from health care administrative database.