Bibliographic record
Abstract
Abstract Background Machine learning (ML) can detect language impairment. However, it is challenging, if we do not have sufficient language data. To deal with this issue, we suggest using ELiEC or "Emotional Learning‐inspired Ensemble Classifier". It is a new ML algorithms that can learn from a few examples. This paper briefly describes ELiEC (structurally and functionally) and presents preliminary results obtained from employing it on the textual datasets extracted from speech produced by 14 (i.e., five patients and nine healthy control) subjects. We collected the speech samples via a web portal named Talk2Me. Method We have developed a language assessment tool combining natural language processing (NLP) techniques and the ELiEC (as an ML tool). NLP techniques extract the total number of word tokens and the total number of unique word types, the total number of sentences, and the total number of syllables. Using the above parameters, we calculate lexical features such as the lexical diversity score, Brunet’s Index (BI), Honore’s Statistic (HS) , Flesch‐Kincaid, and the Flesch Reading‐Ease (FRES) Test readability scores. ELiEC maps linguistic features to patients and healthy controls. In more detail, the ELiEC, which is an ensemble classifier, is developed based on LeDoux’s emotional theory. The theory describes the neural structures underlying the processing of threatening stimuli (see Figure 1) We develop ELiEC by combining classifiers (see Figure 2) according to the interconnections between regions of the brain that are responsible for processing threatening stimuli. Result Table 1 compares results obtained from using various ML tools such as Support Vector Machines (SVMs), k nearest neighbor, and ELiEC to distinguish patients and healthy subjects. As observed, ELiEC’s accuracy is 0.87 (+/‐ 0.33). Thus, compared to other ML tools, it can associate language impairment to people with dementia with the highest accuracy and when there is not a sufficient amount of language data from patients. Conclusion Our results verified that by utilizing ELiEC, we could develop an accurate MLbased language assessment tool when there is a limitation in recruiting patients. We will improve the ELiEC framework and use it to distinguish people with different types of dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".