Multi-Task Learning for Symptom Name Recognition and Severity Assessment in Electronic Medical Records (Preprint)

BACKGROUND Severity classification of diseases and symptoms in electronic medical records (EMRs) is very important in medicine and the life sciences, as it facilitates an easier understanding of medical documents by physicians. However, existing methods perform symptom name recognition and severity assessment tasks separately, which requires very large amounts of expert time and effort and neglects the rich correlations in information between tasks. OBJECTIVE The task of predicting symptom name and severity simultaneously from informative but noisy EMRs is important yet challenging in practice. There is a strong motivation to develop new methods that can effectively perform these two tasks. METHODS In this paper, we explore multi-task learning approaches to integrate symptom name recognition and severity assessment in a unified framework, motivated by the fact that these two tasks can benefit each other. To fulfill the goal of learn the correlation between these two tasks, we propose a novel cluster-based knowledge-aware learning scheme to reduce semantic ambiguity for name recognition and enrich sentence representation learning for severity assessment. RESULTS Symptom classification emerges from the cooperation of several machine learning modes and from the ontology we have developed and released. The experiments performed on synthetic dataset demonstrate the effectiveness of the proposed method and the improved performance of both tasks. We also consider a practical testbed application - symptom severity assessment and diagnosis inference - to test and validate our method and assess its impact in real-world clinical settings. CONCLUSIONS Our proposed model can provide symptom knowledge and implications for clinicians and patients as a reference and has remarkable applicability and generality, outperforming competitors and defining the state-of-the-art. The gastrointestinal ontology and severity assessment corpus are accessible via: https://github.com/shenyingpku/MTL CLINICALTRIAL N/A