Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes

Electronic health records (EHRs) provide a potentially valuable source of information for pharmacovigilance. However, adverse drug events (ADEs), which can be encoded in EHRs with specific diagnosis codes, are heavily under-reported. To provide more accurate estimates for drug safety surveillance, machine learning systems that are able to detect ADEs could be used to identify and suggest missing ADE-specific diagnosis codes. A fundamental consideration when building such systems is how to represent the EHR data to allow for accurate predictive modeling. In this study, two types of clinical code are used to represent drugs and diagnoses: the Anatomical Therapeutic Chemical Classification System (ATC) and the International Statistical Classification of Diseases and Health Problems (ICD). More specifically, it is investigated whether their hierarchical structure can be exploited to improve predictive performance. The use of random forests with feature sets that include only the original, low-level, codes is compared to using random forests with feature sets that contain all levels in the hierarchies. An empirical investigation using thirty datasets with different ADE targets is presented, demonstrating that the predictive performance, in terms of accuracy and area under ROC curve, can be significantly improved by exploiting codes on all levels in the hierarchies, compared to using only the low-level encoding. A further analysis is presented in which two strategies are employed for adding features level-wise according to the concept hierarchies: top-down, starting with the highest abstraction levels, and bottom-up, starting with the most specific encoding. The main finding from this subsequent analysis is that predictive performance can be kept at a high level even without employing the more specific levels in the concept hierarchies.