Prediction of pregnancy outcomes in women with systemic lupus erythematosus before pregnancy: Application of machine learning models
Bibliographic record
Abstract
Introduction: Fetal loss is possible during pregnancy in women with systemic lupus erythematosus (SLE). Predicting pregnancy outcomes for women with SLE can be an effective aid in providing consultation and treatment services. Therefore, this study aimed to develop a machine-learning model that could predict pregnancy outcomes before pregnancy in women with SLE. Methods: The data of all pregnant women referred to the rheumatology center of Shariati Hospital and a specialized rheumatology clinic since 1980 were retrospectively collected from their medical records. Data collection was done by gathering 26 variables that affect pregnancy outcomes. Then, we used standard algorithms to select important features that affect pregnancy outcomes before pregnancy (11 different feature sets). A variety of machine learning algorithms were trained using both imbalanced and balanced datasets in Clementine and Weka software. Finally, the model with a higher area under the receiver operating characteristic curve (AUC) and F-score was selected to predict pregnancy outcomes. Results: Out of 149 pregnancies, 46 pregnancies resulted in spontaneous abortion, while 103 pregnancies resulted in live birth. Compared with other models, the Chi-square automatic interaction detection (CHAID) decision tree was selected as the best-performing model with higher accuracy (93.5 %), specificity (92.9 %), sensitivity (93.8 %), precision (97 %), F-score (0.95), and AUC (0.96). Conclusion: By using the CHAID decision tree to predict the outcome of pregnancy in women with SLE and extracted rules, it is possible to use appropriate methods that prevent spontaneous abortion and also provide timely consultation to women with SLE for making decisions to become pregnant.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".