Automatic Classification of Electronic Nursing Narrative Records Based on Japanese Standard Terminology for Nursing
Why this work is in the frame
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Bibliographic record
Abstract
In Japan, nursing records are not easily put to secondary use because nursing documentation is not standardized. In recent years, electronic health records have necessitated the creation of Japanese nursing terminology. The purpose of this study was to develop and evaluate an automatic classification system for narrative nursing records using natural language processing technology and machine learning. We collected a week's worth of narrative nursing records from an academic hospital. The authors independently annotated the text data, dividing it into morphemes, the smallest meaningful unit in a language. During preprocessing when creating feature quantities, we used a Japanese tokenizer, MeCab, an open-source morphological parser, and the bag-of-words model. A support vector machine was adopted as a classifier for machine learning. The accuracy was 0.96 and 0.86 on the training set and test set, respectively, and the F value was 0.82. Our findings provide useful information regarding the development of an automatic classification system for Japanese nursing records using nursing terminology and natural language processing techniques.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it