MedCare: Leveraging Medication Similarity for Disease Prediction

Dipanwita Dasguptaand Nitesh V. Chawla
Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Publication Date: 
October, 2016

The emergence of electronic health records (EHRs) has made medical history including past and current diseases, and prescribed medications easily available. This has facilitated development of personalized and population health care management systems. Contemporary disease prediction systems leverage data such as disease diagnoses codes to compute patients’ similarity and predict the possible future disease risks of an
individual. However, we posit that not all diseases (such as preexisting conditions) may be represented in an EHR as a disease diagnosis code. It is likely that a patient is already taking a medication but does not have a corresponding disease in the EHR. To that end, we posit that the medication history can serve as a proxy for disease diagnoses, and ask the question whether medication and disease diagnoses combined together can improve the predictability of such systems. Building on our prior work in predicting disease risks (CARE), we develop two disease prediction systems: one using medication-based similarity (medCARE) and the other using both disease and medication-based similarity (combinedCARE). We show that combinedCARE provided a greater coverage and a higher average rank.