Active learning for medical code assignment

• URL: http://arxiv.org/abs/2104.05741v1
• Date: Mon, 12 Apr 2021 18:11:17 GMT
• Title: Active learning for medical code assignment
• Authors: Martha Dais Ferreira, Michal Malyska, Nicola Sahar, Riccardo Miotto, Fernando Paulovich, Evangelos Milios
• Abstract summary: We demonstrate the effectiveness of Active Learning (AL) in multi-label text classification in the clinical domain. We apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set.
• Score: 55.99831806138029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Learning (ML) is widely used to automatically extract meaningful information from Electronic Health Records (EHR) to support operational, clinical, and financial decision-making. However, ML models require a large number of annotated examples to provide satisfactory results, which is not possible in most healthcare scenarios due to the high cost of clinician-labeled data. Active Learning (AL) is a process of selecting the most informative instances to be labeled by an expert to further train a supervised algorithm. We demonstrate the effectiveness of AL in multi-label text classification in the clinical domain. In this context, we apply a set of well-known AL methods to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results show that the selection of informative instances provides satisfactory classification with a significantly reduced training set (8.3\% of the total instances). We conclude that AL methods can significantly reduce the manual annotation cost while preserving model performance.

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