Mucopolysaccharidosis type II detection by Naïve Bayes Classifier: An example of patient classification for a rare disease using electronic medical records from the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
Identifying patients with rare diseases associated with common symptoms is challenging. Hunter syndrome, or Mucopolysaccharidosis type II is a progressive rare disease caused by a deficiency in the activity of the lysosomal enzyme, iduronate 2-sulphatase. It is inherited in an X-linked manner resulting in males being significantly affected. Expression in females varies with the majority being unaffected although symptoms may emerge over time. We developed a Naïve Bayes classification (NBC) algorithm utilizing the clinical diagnosis and symptoms of patients contained within their de-identified and unstructured electronic medical records (EMR) extracted by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). To do so, we created a training dataset using published results in the scientific literature and from all MPS II symptoms and applied the training dataset and its independent features to compute the conditional posterior probabilities of having MPS II disease as a categorical dependent variable for 506497 male patients. The classifier identified 125 patients with the highest likelihood for having the disease and 18 features were selected to be necessary for forecasting. Next, a Recursive Backward Feature Elimination algorithm was employed, for optimal input features of the NBC model, using a k-fold Cross-Validation with 3 replicates. The accuracy of the final model was estimated by the Validation Set Approach technique and the bootstrap resampling. We also investigated that whether the NBC is as accurate as three other Bayesian networks. The Naïve Bayes Classifier appears to be an efficient algorithm in assisting physicians with the diagnosis of Hunter syndrome allowing optimal patient management.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".