论文提出了两个从电子病历里自动判断骨关节炎疼痛等级的工具，SPaDe和PLeDO，不用额外检查就能分级。
该研究提出SPaDe工具，基于同义词匹配从电子病历的非结构化图表笔记中识别疼痛表达，将患者分为轻度或中重度疼痛。进一步整合结构化EMR中的用药信息与笔记中的疼痛量表，形成改进版工具PLeDO。研究使用人类标注的金标准数据验证，SPaDe和PLeDO均能有效区分疼痛等级。
PLeDO: Pain Level Detection for Osteoarthritis from EMR Data
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.