Boundary Detection by Determining the Difference of Classification Probabilities of Sequences: Topic Segmentation of Clinical Notes
Bibliographic record
Abstract
Topic segmentation of clinical notes is a significant issue in the information retrieval domain that could effectively help the process of diagnosis. In this study, we propose a methodology of topic segmentation to clinical notes with boundary detection by determining the difference of classification probabilities of sequences. With 1127 text plain clinical notes collected from I2B2 we experiment on 5 topics: medications, history, hospital course, laboratories and physical exams. The Naive Bayes and Linear SVM models with a selected feature of BOW are employed to train Topic Score Predictors that assign each sequence with a 5-dimensional vector vi in which each element represents the probability of the sequence belonging to a corresponding class. By analyzing the vector ρ = [v1, v2, ......vi], the boundaries would be detected by finding the locations where topic scores have a rapid change. Famous Windiff, Pk and F1Score metrics are used for evaluating our system. Segmenter based on Naive Bayes shows superior performance to that based on SVM model having 0.1468 for Windiff, 0.1221 for Pk and averaged F1Score over 0.90.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".