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 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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".