# PLeDO: Pain Level Detection for Osteoarthritis from EMR Data

arXiv: https://arxiv.org/abs/2608.15719 · HTML: https://mlsanity.com/paper/2608.15719/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 16 Aug 2026.

## Builds on (most cited first, 13 of 13)

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- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [A new paradigm for accelerating clinical data science at Stanford Medicine](https://mlsanity.com/paper/2003.10534/) (2020)
- [Explaining Black-box Models for Biomedical Text Classification](https://mlsanity.com/paper/2012.10928/) (2020)
- [Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy](https://mlsanity.com/paper/2303.13722/) (2023)
- [Measuring Pain in Sickle Cell Disease using Clinical Text](https://mlsanity.com/paper/2008.11081/) (2020)
- [Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model](https://mlsanity.com/paper/2403.17199/) (2024)
- [AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach](https://mlsanity.com/paper/2512.16739/) (2025)

## Abstract (arXiv)

Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not able to normally repair themselves. The aim of this pilot research study is to understand the pain severity for OA from patients' primary care Electronic Medical Records (EMR), both from the structured medical data and the unstructured chart note data using information extraction, natural language processing and machine learning techniques. We propose SPaDe, a Synonym-based Pain level Detection tool to categorize patients into having mild or moderate-to-severe pain to understand diagnosis and treatment methods based on only the pain related expressions in the unstructured chart note. Expressions are subjective, objective, and influenced by cultural background and demography which poses a difficult challenge. Therefore, we improve the model by incorporating the medication information from the structured EMR data and pain scale related information from the chart note to propose an integrated pain level detection tool for OA called PLeDO. With the help of human labeled gold standard data, we demonstrate that both SPaDe and PLeDO can detect mild and moderate-to-severe pain from the EMR data to analyze and potentially improve the quality of care in primary care setting.