Multilingual automation of transcript preprocessing in Alzheimer's disease detection
Bibliographic record
Abstract
INTRODUCTION: Analyzing linguistic functions can improve early detection of Alzheimer's disease (AD). To date, no studies have focused on creating a universal pipeline for clinical transcript preprocessing. METHODS: This article presents a simple and efficient method for processing linguistic and phonetic data, sequencing subproblems of cleaning, normalization, and measure extraction tasks. Because some of these tasks are language- and context- dependent, they were designed to be easily configurable, thus increasing their scalability when dealing with new corpora. RESULTS: Results show improved performances over previous studies in this time-consuming preprocessing task. Moreover, our findings showed that some discursive markers extracted from transcripts revealed a significant correlation (>0.5) with cognitive impairment severity. DISCUSSION: This article contributes to the literature on AD by presenting an efficient pipeline that allows speeding up the transcripts preprocessing task. We further invite other researchers to contribute to this work to help improve the quality of this pipeline (https://github.com/LiNCS-lab/usAge).
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".