Explainable Prediction of Medical Codes With Knowledge Graphs
Explainable Prediction of Medical Codes With Knowledge Graphs (frontiers in Bioengineering and Biotechnology).
International Classification of Diseases (ICD) is an authoritative health care classification system of different diseases. It is widely used for disease and health records, assisted medical reimbursement decisions, and collecting morbidity and mortality statistics. The most existing ICD coding models only
translate the simple diagnosis descriptions into ICD codes. And it obscures the
reasons and details behind specific diagnoses. Besides, the label (code)
distribution is uneven. And there is a dependency between labels. Based on the
above considerations, the knowledge graph and attention mechanism were expanded
into medical code prediction to improve interpretability. In this study, a new
method called G_Coder was presented, which mainly consists of Multi-CNN, graph
presentation, attentional matching, and adversarial learning.
Quelle: frontiers in Bioengineering and Biotechnology, 14.08.2020