HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets
This addresses data challenges in smart healthcare for improved medical services, but it appears incremental as it builds on existing transfer learning and autoencoder methods.
The paper tackles the problem of high sparsity and heterogeneity in healthcare data by proposing HTPS, a system that uses feature engineering and autoencoders for knowledge transfer, resulting in outperformance over benchmarks on various prediction tasks and datasets.
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.