KEPTlongfomer is a medical knowledge enhanced version of Longformer that was further pre-trained using contrastive learning. The model achieves SOTA performance on auto ICD coding on MIMIC-III as of 11/12/2022. A sister model for better performance is available here.
Pre-training
We initialized this model from clinical longformer.
And then pretrained with Hierarchical Self-Alignment Pretrain (HSAP) using Knowledge Graph UMLS. This includes (a) Hierarchy, (b) Synonym, (c) Abbreviation. For more info, see section 3.3 in paper. The learning rate was 5e-5, weight decay was 0.01, adam epsilon was 1e-5.
Usage
See our github for how to use this with prompts on auto ICD coding.
With the following result:
| Metric | Score |
|---|---|
| rec_micro | =0.5729403619819988 |
| rec_macro | =0.11342156911120573 |
| rec_at_8 | =0.4094837705486378 |
| rec_at_75 | =0.8470734920535119 |
| rec_at_50 | =0.8005338782352 |
| rec_at_5 | =0.2891628170355805 |
| rec_at_15 | =0.5768778119750537 |
| prec_micro | =0.6411968713105065 |
| prec_macro | =0.12227610414493029 |
| prec_at_8 | =0.7760972716488731 |
| prec_at_75 | =0.197504942665085 |
| prec_at_50 | =0.2768090154211151 |
| prec_at_5 | =0.8483392645314354 |
| prec_at_15 | =0.6178529062870699 |
| f1_micro | =0.6051499904242899 |
| f1_macro | =0.11768251595637802 |
| f1_at_8 | =0.536107150495997 |
| f1_at_75 | =0.32032290907137506 |
| f1_at_50 | =0.411373195944102 |
| f1_at_5 | =0.43131028155283435 |
| f1_at_15 | =0.5966627077602488 |
| auc_micro | =0.9651754312635265 |
| auc_macro | =0.8566590059725866 |
| acc_micro | =0.43384592341105344 |
| acc_macro | =0.08639139221100567 |
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