Multi-label text classification via secondary use of large clinical real-world data sets.

ABSTRACT: Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logis

SUBMITTER: Veeranki SPK 

PROVIDER: S-EPMC11541716 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature