Towards Automated ICD Coding Using Deep Learning
Table 3: Attention allocation on one sample. Top shows all the extracted diagnosis descriptions from written discharge diagnosis given by physicians. Down shows the attention allocation on a subset of ICD codes. The codes in bold format have true labels.
['LONG TITLE OF ICD CODE', '1', '2', '3', '4', '[b] 5']
We can observe that for different ICD codes, our model allocates different attention scores to diagnosis descriptions automatically. For example, when assigning the ICD code titled ‘Neonatal jaundice associated with preterm delivery’, the model puts more attention on ‘Prematurity at 34 and 5/7 weeks gestation’ and ‘Hyperbilirubinemia of prematurity’, while less attention on other irrelevant diagnosis descriptions like ‘Sepsis ruled out’. The attention allocation results of all 50 the ICD codes for this sample can be found in the supplementary materials.