ClinicNet: Clinical Practice Oriented Medical Representation Learning for Electronic Medical Records

Medical representation learning with deep learning methods is a popular research topic in recent years. Researchers build complex deep learning-based models for learning health status representation from electronic medical records (EMR) and performing downstream clinical prediction tasks. Previous works have achieved impressive performance on various clinical prediction tasks. However, almost no work analyzes about their performance in clinical practice. Nevertheless, as a form of clinically assisted decision making, an important target for clinical prediction is giving useful information for physicians when diagnosing patients. In order to eliminate this gap, we propose ClinicNet, an end-to-end deep representation learning framework for personalized and clinical practice-oriented health status representation learning from EMR. With analysis in real clinical scenes, the health status representation learned by ClinicNet is closer to the need of medical practice with specially designed loss function in training. Furthermore, verified by experiments on real-world datasets, ClinicNet achieves competitive performance compared with previous works for clinical prediction tasks.