Label Accuracy in Electronic Health Records and Its Impact on Machine Learning Models for Early Prediction of Gestational Diabetes: 3-Step Retrospective Validation Study
Bibliographic record
Abstract
Background: Several studies have used electronic health records (EHRs) to build machine learning models predicting the likelihood of developing gestational diabetes mellitus (GDM) later in pregnancy, but none have described validation of the GDM "label" within the EHRs. Objective: This study examines the accuracy of GDM diagnoses in EHRs compared with a clinical team database (CTD) and their impact on machine learning models. Methods: EHRs from 2018 to 2022 were validated against CTD data to identify true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Logistic regression models were trained and tested using both EHR and validated labels, whereafter simulated label noise was introduced to increase FP and FN rates. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC) and average precision (AP). Results: Among 3952 patients, 3388 (85.7%) were correctly identified with GDM in both databases, while 564 cases lacked a GDM label in EHRs, and 771 were missing a corresponding CTD label. Overall, 32,928 (87.5%) of cases were TN, 3388 (9%) TP, 771 (2%) FP, and 564 (1.5%) FN. The model trained and tested with validated labels achieved an ROC AUC of 0.817 and an AP of 0.450, whereas the same model tested using EHR labels achieved 0.814 and 0.395, respectively. Increased label noise during training led to gradual declines in ROC AUC and AP, while noise in the test set, especially elevated FP rates, resulted in marked performance drops. Conclusions: Discrepancies between EHR and CTD diagnoses had a limited impact on model training but significantly affected performance evaluation when present in the test set, emphasizing the importance of accurate data validation.
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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 | 0.055 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".