PLeDO：基于电子病历数据的骨关节炎疼痛程度检测
PLeDO: Pain Level Detection for Osteoarthritis from EMR Data
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中文总结 AI 辅助
本研究针对骨关节炎疼痛检测难题，提出SPaDe及集成药物、疼痛量表信息的PLeDO工具，可从电子病历中检测疼痛程度，有望提升初级保健护理质量。
中文摘要 AI 辅助
骨关节炎（OA）是一种进展性慢性关节疾病，当受损关节组织无法正常自我修复时，会导致关节软骨和骨组织破损。本试点研究旨在利用信息抽取、自然语言处理和机器学习技术，从患者初级保健电子病历（EMR）的结构化医疗数据和非结构化病历记录数据中，理解OA患者的疼痛严重程度。我们提出了SPaDe，这是一种基于同义词的疼痛程度检测工具，仅通过非结构化病历记录中的疼痛相关表达，将患者分为轻度疼痛或中重度疼痛，以基于疼痛情况理解诊断和治疗方法。疼痛表达具有主观性、客观性，且受文化背景和人口统计学因素影响，这带来了巨大挑战。因此，我们结合EMR结构化数据中的药物信息和病历记录中的疼痛量表相关信息改进模型，提出了针对OA的集成疼痛程度检测工具PLeDO。借助人工标注的金标准数据，我们证明SPaDe和PLeDO均可从EMR数据中检测轻度和中重度疼痛，用于分析并潜在改善初级保健环境的护理质量。
英文摘要
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.
