Divide-n-Discover: Discretization based data exploration framework for healthcare analytics

Insightful and principled visualization techniques may successfully help complex clinical data exploration tasks and aid in the process of knowledge discovery. In this paper, we propose a framework Divide-n-Discover to visualize and explore clinical data effectively, and demonstrate its effectiveness in predicting readmission risk for Congestive Heart Failure patients. Our proposed method provides clinicians a mechanism to dynamically explore the data and to understand how a single factor may influence the risk of readmission for a given patient. For example, our study indicates that patients between age 47 and 48 have 2.63 time higher chance of getting readmitted to the hospital within 30 days, compared to other patients; likewise, patients with length of stay above 13 days are 2.27 times more likely to be readmitted within 30 days. The finding suggests that hospitals might be under pressure to discharge patients within two week while some patients may benefit from a longer stay. These observations may become valid hypotheses leading to further clinical investigation or discoveries. To the best of our knowledge, this is the first ever work that proposes principled discretization and visualization techniques in the hospital readmission risk prediction problem.