Automatic Diagnosis of Hip and Knee Osteoarthritis from Medical Text Records
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".