Medical internet of things leads to revolutionary im- provements 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 spar-
sity 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.

本文提出的 Heterogeneous Transferring Prediction System（HTPS）采用特征工程机制将医疗数据转化成稀疏和密集特征矩阵，并应用自编码器网络实现特征嵌入和来自异构数据集的知识移植，能在各种预测任务和数据集上优于基准系统，实验结果显示异构数据对系统的负面影响以及 HTPS 的高可转移性。