EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation
Abstract
Assigning standard medical codes (e.g., ICD-9-CM) representing diagnoses or procedures to electronic health record (EHR) is an important task in the medical domain. However, automatic coding is difficult since the clinical note is composed of multiple long and heterogeneous textual narratives (e.g., discharge diagnosis, pathology reports, surgical procedure notes). Furthermore, the code label space is large and the label distribution is extremely unbalanced. The state-of-the-art methods mainly regard EHR coding as a multi-label text classification task and use shallow convolution neural network with fixed window size, which is incapable of learning variable n-gram features and the ontology structure between codes. In this paper, we leverage a densely connected convolutional neural network which is able to produce variable n-gram features for clinical note feature learning. We also incorporate a multi-scale feature attention to adaptively select multi-scale features since the most informative n-grams in clinical notes for each word can vary in length according to the neighborhood. Furthermore, we leverage graph convolutional neural network to capture both the hierarchical relationships among medical codes and the semantics of each code. Finally, We validate our method on the public dataset, and the evaluation results indicate that our method can significantly outperform other state-of-the-art models.
Read the paper
Related studies
- Regulation of nucleolus assembly by non-coding RNA polymerase II transcripts
- For a Healthy Histone Code, a Little SUMO in the Tail Keeps the Acetyl Away
- Discovering a binary CTCF code with a little help from BORIS
- Article Commentary: Stabilizing the Code–-Methods to Preserve RNA Prove Their Worth
- Foreword to the Special Issue for the RINTC (The Implicit Seismic Risk of Code-Conforming Structures) Project
- Commentary: Promoting Health and Well-being in Pediatric Primary Care Settings: Using Health and Behavior Codes at Routine Well-child Visits
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.