mlsanityData to 7 Oct 2026

Active learning for medical code assignment

arXiv 2104.05741 ↗First posted 12 Apr 2021cs.LG cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 12 Apr 2021.

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15 of 42 references.

References · 15

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3 more
  1. 2018An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notescited by 16
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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Automated Clinical Coding: What, Why, and Where We Are?cited by 33
  2. 2022A Unified Review of Deep Learning for Automated Medical Codingcited by 26