Balancing Model Complexity and Clinical Deployability in Deep Learning for Sociodemographic Information Extraction
Bibliographic record
Abstract
Sociodemographic factors are critical determinants of health outcomes and disparities, yet their documentation in electronic medical records is often sparse and confined to unstructured clinical text. This poses substantial challenges for automated extraction and integration into clinical decision-making. In this study, we systematically evaluate and compare 6 convolutional neural network architectures, including hybrid models that integrate traditional classifiers, for binary classification of multiple sociodemographic characteristics from EMR text using data from 4375 patients across 96 primary care clinics. The goal was to assess how model complexity and lexical diversity influence classification performance. Manual annotation achieved high inter-rater reliability (kappa: 0.98 for documentation status, 0.96 for documented information). We report performance using F1 score, precision, recall, area under the precision-recall curve, and Matthews correlation coefficient. Results showed that simpler architectures, particularly a single-layer CNN, consistently outperform deeper or hybrid models across most characteristics (F1 score: 90.99%), especially under conditions of data imbalance and varied documentation patterns. While hybrid models offered gains for well-documented factors like marital status, they were less effective for sparse or diverse characteristics. These findings provide a practical framework for developing efficient, interpretable clinical NLP pipelines and inform model selection strategies for real-world health equity and EMR research applications.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".