Early identification of Family Medicine residents at risk of failure using Natural Language Processing and Explainable Artificial Intelligence
Bibliographic record
Abstract
Abstract Background During residency, each resident is observed and receives feedback based on their performance. Residency training is demanding, with some residents struggling with their academic performance. A competency-based residency training program’s success depends on its ability to identify residents with difficulty during their first year of post-graduate education and to provide them with timely intervention and support. Objective In large training programs such as Family Medicine, identifying residents at risk of failing their certification exams is difficult. We developed an AI system using state-of-the-art technologies in Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Explainable AI (XAI) to detect at-risk residents automatically. Materials and Methods The research was conducted in the 2023-24 academic year. We implemented ML, DL and NLP models for prediction and performance analysis. The target variable chosen for the prediction was the determination of whether the resident would fail or pass their certification exam. XAI was used to enhance the understanding of the model’s inner workings. Results In total, there were 1382 data points of residents. The final model, Support Vector Machine (SVM), achieved an accuracy of 89.05% and an F1 score of 74.54 for the multiclass classification when multimodal (text and tabular) data was used. This model outperformed the models that only used qualitative or quantitative data exclusively. Conclusion Combining qualitative and quantitative data represents a novel approach and provided better classification results. This research demonstrates the feasibility of an automated AI system for the early identification of residents at risk of academic struggle. Prior Abstract Presentation Abstract presented at AMEE (An International Association for Medical Education) Conference Basel, Switzerland, August 24-28, 2024.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".