  Electronic health record (EHR) coding is the task of assigning ICD codes to
each EHR. Most previous studies either only focus on the frequent ICD codes or
treat rare and frequent ICD codes in the same way. These methods perform well
on frequent ICD codes but due to the extremely unbalanced distribution of ICD
codes, the performance on rare ones is far from satisfactory. We seek to
improve the performance for both frequent and rare ICD codes by using a
contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR
coding as a few-shot learning task. First, we construct a heterogeneous EHR
word-entity (HEWE) graph for each EHR, where the words and entities extracted
from an EHR serve as nodes and the relations between them serve as edges. Then,
CoGraph learns similarities and dissimilarities between HEWE graphs from
different ICD codes so that information can be transferred among them. In a
few-shot learning scenario, the model only has access to frequent ICD codes
during training, which might force it to encode features that are useful for
frequent ICD codes only. To mitigate this risk, CoGraph devises two graph
contrastive learning schemes, GSCL and GECL, that exploit the HEWE graph
structures so as to encode transferable features. GSCL utilizes the
intra-correlation of different sub-graphs sampled from HEWE graphs while GECL
exploits the inter-correlation among HEWE graphs at different clinical stages.
Experiments on the MIMIC-III benchmark dataset show that CoGraph significantly
outperforms state-of-the-art methods on EHR coding, not only on frequent ICD
codes, but also on rare codes, in terms of several evaluation indicators. On
frequent ICD codes, GSCL and GECL improve the classification accuracy and F1 by
1.31% and 0.61%, respectively, and on rare ICD codes CoGraph has more obvious
improvements by 2.12% and 2.95%.
