Please read Dr. Chen’s article from the 2020 IEEE International Conference on Big Data (Big Data) titled, “Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes.”
Adverse Drug Events (ADEs), defined a s ” an appreciably harmful or unpleasant event resulting from the use or misuse of a drug”, are a serious or even life-threatening problem. According to the Food and Drug Administration (FDA), the number of ADEs reported to FAERS (FDA Adverse Event Reporting System) resulting in death and serious outcomes increase consistently. Statistics show that each year ADEs account for over 3.5 million physician office visits, about 1 million emergency department visits, and approximately 125,000 hospital admissions. For inpatient settings, ADEs account for an estimated 1 in 3 of all hospital adverse events (AE) and affect about 2 million hospital stays each year. To read the full article.
Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes. Shi, X. Gao, C. Ha, Y. Wang, G. Gao and Y. Chen. 2020 IEEE International Conference on Big Data (Big Data) 2020. pp. 1388-1393. DOI: 10.1109/BigData50022.2020.9378336.