Name
Abstract | ||
|---|---|---|
Rapidly growing healthcare big data are becoming a valuable resource for improving clinical activity through technology. Estimation of the severity of diseases and symptoms in electronic medical records is important because it allows physicians to more easily gain an understanding of medical documents. In this paper, motivated by the fact that these two tasks can benefit each other, we propose a clinical multi-task learning approach (CMTL) that integrates severity estimation and symptom name recognition into a unified framework. Specifically, CMTL consists of two key components: (i) a named-entity recognition model that learns rich knowledge-aware entity representations and classifies entity terms into predefined categories; (ii) a severity estimation model that provides the symptom severity stages of patients\u0027 self-reported symptom descriptions. These two tasks work on a shared text-encoding layer; then, a multi-head attention mechanism is proposed to learn the important information from different representation subspaces at different positions. Finally, we propose a shared label-transferring network to enhance their interaction in a multi-task learning setting. We created the first symptom severity estimation dataset (SymptomSE) and a gastrointestinal ontology to evaluate the effectiveness of our model. The experiments demonstrate the effectiveness of the proposed method and the improved performance of both tasks. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313543 | BIBM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang Deng | 1 | 0 | 0.34 |
| Dagang Li | 2 | 0 | 0.34 |
| Qiang Zhang | 3 | 0 | 0.34 |
| Ying Shen | 4 | 5 | 5.49 |