# Natural Language Processing in Biomedicine: A Unified System Architecture Overview

arXiv: https://arxiv.org/abs/1401.0569 · HTML: https://mlsanity.com/paper/1401.0569/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2014.

## Built on by (most cited first, 5 of 5)

- [A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support](https://mlsanity.com/paper/2503.23029/) (2025)
- [Design considerations for a hierarchical semantic compositional framework for medical natural language understanding](https://mlsanity.com/paper/2204.02067/) (2022)
- [Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicos](https://mlsanity.com/paper/2410.00616/) (2024)
- [Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies](https://mlsanity.com/paper/2412.03176/) (2024)
- [Do It Right! A Methodology for Successful NLP System Development](https://mlsanity.com/paper/2607.05644/) (2026)

## Abstract (arXiv)

In modern electronic medical records (EMR) much of the clinically important data - signs and symptoms, symptom severity, disease status, etc. - are not provided in structured data fields, but rather are encoded in clinician generated narrative text. Natural language processing (NLP) provides a means of "unlocking" this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we will review briefly. Additionally, challenges facing current research efforts in biomedical NLP include the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge.