Human Disease Network Analysis of Clinical Treatment Outcomes with Zero Inflation

Music City Center 

There has been significant attention to human disease network (HDN) analysis, which describes how diseases are interconnected. Compared to the gene-centric and phenotypic ones, clinical treatment-based HDNs can have more direct practical relevance. For common cancers, our goal is to conduct the HDN analysis for inpatient and outpatient treatments separately, which have significantly different clinical implications and data patterns. This effort can assist in better understanding not only individual cancers but also their commonalities and differences, which have been scarcely examined under the HDN framework.
We mine SEER-Medicare for subjects diagnosed from 2004 to 2017 with 10 common cancers. In the inpatient/outpatient treatment setting, a total of 113/168 diseases are analyzed. We develop a deep neural network (DNN)-based estimation approach, which adopts an additive two-part loss function to accommodate zero inflation, DNN to accommodate nonlinearity, and penalization to identify network edges. In the data analysis, sensible findings on interconnections and modular structures are made for individual cancers.

clinical treatment outcomes

human disease networks

cancers

SEER-Medicare data 

International Chinese Statistical Association