aiqbench analysis
This research introduces PLeDO, a machine learning framework that integrates structured EMR data with unstructured clinical notes via NLP and synonym detection to accurately classify osteoarthritis pain severity. By addressing challenges like subjective patient expressions and demographic biases, the study demonstrates how advanced AI can enhance diagnostic precision in primary care settings without relying on external sensors or wearables. The work represents a significant step toward leveraging existing healthcare data infrastructure for better chronic disease management through targeted algorithmic improvements.
PLeDO: Pain Level Detection for Osteoarthritis from EMR Data
arXiv:2608.15719v1 Announce Type: cross Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Information Retrieval (cs.IR); Machine Learning (cs.LG)
arXiv:2608.15719 [cs.AI]
(or arXiv:2608.15719v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.15719 arXiv-issued DOI via DataCite (pending registration)
Chen, Y., Cai, J., Sadman, N., Zulkernine, F., Queenan, J., and Barber, D. (2026). PLeDO: Pain level detection for osteoarthritis from EMR data. Intelligent Data Analysis, 1-20
https://doi.org/10.1177/1088467X261472133 DOI(s) linking to related resources
Full text under the publisher's open licence · 260 words
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