Abstract: Based on two health care Big Data sets with sample sizes n=10 million and 50 million respectively, we derived different types of disease-disease networks using the longitudinal information. We establish both short-term and long-term directed networks as well as the simultaneously-occurring undirected network of 1660 PheWAS disease groups. Among 2,753,940 possible disease pairs, we identified 646,969 for long-term and 10,587 for short-term significant pairs,respectively, which were observed in at least five patients and had relative risk (RR) > 1 with significance at 0.05 level after Bonferroni corrections. Among 1,376,970 possible disease pairs of simultaneous occurrence, we identified 18,137 which were observed in at least five patients and had RR > 1 with significance at 0.05 level after Bonferroni corrections. Based on the results, we define a new disease Influence Factor (IF). For the short-term network, the top diseases with the highest IF is more likely pregnancy related; while for the long-term network, it is more kidney related diseases. More clinical implications from these findings will be discussed. I will also discuss the challenges in Big Data research and future trends.