Interconnections in Clinical Treatment Measures at the Pan-Disease Level: A Deep Neural Network Analysis of SEER-Medicare

In outcomes research, clinical treatment measures are routinely analyzed. Most existing studies focus on individual diseases, and increasing recent evidence suggests that clinical treatment measures of different diseases can be “correlated” (or interconnected). Existing studies on such interconnections are usually limited to a small number of diseases. In this study, for U.S. older patients with common cancers, we complement the existing literature and conduct a pan-disease analysis of interconnections in clinical treatment measures. This effort can assist in better managing and planning healthcare resources. We mine SEER-Medicare for subjects diagnosed between January 2004 and December 2017 with esophageal, colorectal, liver, pancreatic, oral cavity/pharyngeal, lung, breast, prostate, renal/kidney, and bladder cancers. In the inpatient/outpatient treatment settings, a total of 113/168 diseases are analyzed. To accommodate unique data characteristics and our analysis goals, we develop a deep neural network (DNN)-based estimation approach, which adopts an additive two-part loss function to accommodate zero inflation, uses DNNs to capture nonlinearity, and applies penalization to identify interconnections. Extensive statistical and numerical investigations are conducted. In the data analysis, sensible findings are made for individual cancers, and across-cancer and across-setting analyses are also conducted. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.