Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models

Abstract

Objective: Surgical registries play a crucial role in clinical knowledge discovery, hospital quality assurance, and quality improvement. However, maintaining a surgical registry requires significant monetary and human resources given the wide gamut of information abstracted from medical records ranging from patient co-morbidities to procedural details to post-operative outcomes. Although natural language processing (NLP) methods such as pretrained language models (PLMs) have promised automation of this process, there are yet substantial barriers to implementation. In particular, constant shifts in both underlying data and required registry content are hurdles to the application of NLP technologies. Materials and Methods: In our work, we evaluate the application of PLMs for automating the population of the Society of Thoracic Surgeons (STSs) adult cardiac surgery registry (ACS) procedural elements, for which we term Cardiovascular Surgery Bidirectional Encoder Representations from Transformers (CS-BERT). CS-BERT was validated across multiple satellite sites and versions of the STS-ACS registry. Results: CS-BERT performed well (F1 score of 0.8417 ± 0.1838) in common cardiac surgery procedures compared to models based on diagnosis codes (F1 score of 0.6130 ± 0.0010). The model also generalized well to satellite sites and across different versions of the STS-ACS registry. Discussion and Conclusions: This study provides evidence that PLMs can be used to extract the more common cardiac surgery procedure variables in the STS-ACS registry, potentially reducing need for expensive human annotation and wide scale dissemination. Further research is needed for rare procedural variables which suffer from both lack of data and variable documentation quality.

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