# Comparative Analysis of Text Classification Approaches in Electronic Health Records

arXiv: https://arxiv.org/abs/2005.06624 · HTML: https://mlsanity.com/paper/2005.06624/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 12 of 12)

- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding](https://mlsanity.com/paper/2304.05368/) (2023)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [Assessment of contextualised representations in detecting outcome phrases in clinical trials](https://mlsanity.com/paper/2203.03547/) (2022)
- [MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations](https://mlsanity.com/paper/2310.12489/) (2023)
- [Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods](https://mlsanity.com/paper/2209.00470/) (2022)
- [Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports](https://mlsanity.com/paper/2309.12273/) (2023)
- [Multi-objective Representation for Numbers in Clinical Narratives: A CamemBERT-Bio-Based Alternative to Large-Scale LLMs](https://mlsanity.com/paper/2405.18448/) (2024)
- [Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study](https://mlsanity.com/paper/2408.17181/) (2024)
- [A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting](https://mlsanity.com/paper/2411.16702/) (2024)
- [From Staff Messages to Actionable Insights: A Multi-Stage LLM Classification Framework for Healthcare Analytics](https://mlsanity.com/paper/2509.05484/) (2025)

## Abstract (arXiv)

Text classification tasks which aim at harvesting and/or organizing information from electronic health records are pivotal to support clinical and translational research. However these present specific challenges compared to other classification tasks, notably due to the particular nature of the medical lexicon and language used in clinical records. Recent advances in embedding methods have shown promising results for several clinical tasks, yet there is no exhaustive comparison of such approaches with other commonly used word representations and classification models. In this work, we analyse the impact of various word representations, text pre-processing and classification algorithms on the performance of four different text classification tasks. The results show that traditional approaches, when tailored to the specific language and structure of the text inherent to the classification task, can achieve or exceed the performance of more recent ones based on contextual embeddings such as BERT.