Method to the automatic International Classification of Diseases (ICD) coding for clinical records

A system to classify a plurality of clinical records into International Classification of Diseases (ICD) codes, the system comprising:

• one or more processor(s);
• and
• a memory communicatively coupled to the processor(s), wherein the memory stores instructions executed by the processor, wherein the memory comprising:
• a generator (G) to generate one or more synthetic features corresponding to one or more ICD code descriptions;
• a feature extractor to extract one or more real latent features from a plurality of clinical documents and generates one or more real features by training a plurality of generative adversarial networks (GANs), wherein the generator (G) generates synthesized features after the GANs are trained and calibrate a binary code classifier with the real latent features generated by the feature extractor for a low-shot ICD code l, wherein the GANs improve the low-shot ICD code l by generating a plurality of pseudo data examples in a latent feature space of the clinical documents for the low-shot ICD codes l, wherein the generator (G) generates one or more code-specific latent features conditioned on a textual description of each ICD code descriptions by using a Wasserstein GAN with gradient penalty (WGAN-GP), wherein the Wasserstein GAN with gradient penalty (WGAN-GP) generates a latent feature vector (f);
• a discriminator (D) to distinguish between the synthesized features generated by the generator (G) and the real features generated by the feature extractor and determines whether the features are the real features generated by the feature extractor or the synthetic features generated by the generator (G);
• a label encoder to encode a sequence of a plurality of keywords in the ICD code description into a sequence of one or more hidden state sequences by using a long short-term memory (LSTM);
• and
• a keywords reconstructor to reconstruct the keywords extracted from the clinical documents associated with a code l to ensure the latent feature vector (f) captures a semantic meaning of a code l.