A hybrid system to understand the relations between assessments and plans in progress notes

Jifan Gao, Shilu He, Junjie Hu, Guanhua Chen

Abstract:

Objective: The paper presents a novel solution to the 2022 National NLP Clinical Challenges (n2c2) Track 3,
which aims to predict the relations between assessment and plan subsections in progress notes.
Methods: Our approach goes beyond standard transformer models and incorporates external information such
as medical ontology and order information to comprehend the semantics of progress notes. We fine-tuned
transformers to understand the textual data and incorporated medical ontology concepts and their relationships
to enhance the model’s accuracy. We also captured order information that regular transformers cannot by
taking into account the position of the assessment and plan subsections in progress notes.
Results: Our submission earned third place in the challenge phase with a macro-F1 score of 0.811. After
refining our pipeline further, we achieved a macro-F1 of 0.826, outperforming the top-performing system
during the challenge phase.
Conclusion: Our approach, which combines fine-tuned transformers, medical ontology, and order information,
outperformed other systems in predicting the relationships between assessment and plan subsections in progress
notes. This highlights the importance of incorporating external information beyond textual data in natural
language processing (NLP) tasks related to medical documentation. Our work could potentially improve the
efficiency and accuracy of progress note analysis.