# Clinical Concept Extraction for Document-Level Coding

arXiv: https://arxiv.org/abs/1906.03380 · HTML: https://mlsanity.com/paper/1906.03380/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Automated Clinical Coding: What, Why, and Where We Are?](https://mlsanity.com/paper/2203.11092/) (2022)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes](https://mlsanity.com/paper/2403.05795/) (2024)
- [Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review](https://mlsanity.com/paper/2412.18043/) (2024)
- [Faithful and Plausible Explanations of Medical Code Predictions](https://mlsanity.com/paper/2104.07894/) (2021)
- [Automatic Coding at Scale: Design and Deployment of a Nationwide System for Normalizing Referrals in the Chilean Public Healthcare System](https://mlsanity.com/paper/2307.05560/) (2023)
- [RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records](https://mlsanity.com/paper/2507.15867/) (2025)

## Abstract (arXiv)

The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been information extraction: linking spans of text to concepts in a detailed domain ontology. However, recent work has demonstrated the potential of supervised machine learning to extract document-level codes directly from the raw text of clinical notes. We propose to bridge the gap between the two approaches with two novel syntheses: (1) treating extracted concepts as features, which are used to supplement or replace the text of the note; (2) treating extracted concepts as labels, which are used to learn a better representation of the text. Unfortunately, the resulting concepts do not yield performance gains on the document-level clinical coding task. We explore possible explanations and future research directions.