  Medical internet of things leads to revolutionary improvements in medical
services, also known as smart healthcare. With the big healthcare data, data
mining and machine learning can assist wellness management and intelligent
diagnosis, and achieve the P4-medicine. However, healthcare data has high
sparsity and heterogeneity. In this paper, we propose a Heterogeneous
Transferring Prediction System (HTPS). Feature engineering mechanism transforms
the dataset into sparse and dense feature matrices, and autoencoders in the
embedding networks not only embed features but also transfer knowledge from
heterogeneous datasets. Experimental results show that the proposed HTPS
outperforms the benchmark systems on various prediction tasks and datasets, and
ablation studies present the effectiveness of each designed mechanism.
Experimental results demonstrate the negative impact of heterogeneous data on
benchmark systems and the high transferability of the proposed HTPS.
