Leveraging TFR-BERT for ICD diagnoses ranking - Silva, Ana (2023)
This work describes applying a transformer-based ranking solution to the specific problem of ordering ICD diagnoses codes. Taking advantage of the TFR-BERT framework and adapting it to the biomedical context using pre-trained and publicly available language representation models, namely BioBERT, BlueBERT and ClinicalBERT (Bio + Discharge Summary BERT Model), we demonstrate the effectiveness of such a framework and the strengths of using pre-trained models adapted to the biomedical domain. We showcase this by using a benchmark dataset in the healthcare field—MIMIC-III—showing how it was possible to learn how to sequence the main or primary diagnoses and the order in which the secondary diagnoses are presented. A window-based approach and a summary approach (using only the sentences with diagnoses) were also tested in an attempt to circumvent the maximum sequence length limitation of BERT-based models. BioBERT demonstrated superior performance in all approaches, achieving the best results in the summary approach.