SERG: A Sequence-to-Sequence Model for Chinese ECG Report Generation
Cong Yang, Ziliang Li, Haoyi Fan, Yongxia Chen, Nan Lin, Na Wei · 2022
Medical report generation is a rising application of big data, which mitigates burden on doctors in clinical trail. However, the serious imbalanced distribution of diseases leads to data bias, an important issue in qualified medical reports generation. To address this issue, a sequence-to-sequence model with incorporation of clinical experience is proposed to generate Chinese ElectroCardioGraph (ECG) reports. The proposed model consists of Electrocardiograph Feature Extractor (EFE), Posterior Knowledge Embedding (PKE) and Report Generator (RG). Firstly, we introduce a novel spatial-temporal information fusion module in EFE to extract robust features from ECG data. Then, embeddings of ECG tags extracted from clinical ECG reports combined with output of EFE are then feed into PKE, which builds a bridge between ECG tags and ECG data, alleviating the problem caused by data bias. Finally, a transformer-based decoder is used in RG to generate ECG reports step by step with output of PKE as key and value. Experiments conducted on private data show that the proposed model can obtain an accuracy 50.26% on BLEU-4, 7.69% higher than state-of-the-art. Our method can also achieve better fluent reports, as demonstrated by the performance on CIDEr, a commonly used content metric.