Automatic Diagnosis of Hip and Knee Osteoarthritis from Medical Text Records

Osteoarthritis (OA) is a highly frequent musculoskeletal condition defined by the progressive degradation of joint cartilage and the underlying bone, resulting in the manifestation of pain and functional limitations. Identification of symptoms as early as possible for timely intervention is critical for effective pain management and treatment. Knee and hip OA are common in older patients which can greatly affect their mobility, lifestyle, and lead to other health complications. Electronic Medical Records (EMR) in primary care settings contain patients’ structured historical data including unstructured text data in the encounter chart notes. The unstructured notes are often very long, compiled from multiple patient-physician encounters, and contain medical jargon including personal data. The data offers a variety of computational challenges but it contains valuable information for disease diagnosis especially for detecting hip or knee OA. We demonstrate multiple keyword-based strategies to detect the OA-affected bone joints, including a simple rule-based approach and a machine learning based approach. We also provide an ablation study to show the effects of the different natural language text processing methods and validate our results against gold standard data labelled by a human expert. Our Random Forest (RF) model achieved the best result of 74.89% F1-score with OA related paragraph extraction.