Detection of cognitive impairment using a machine-learning algorithm
Bibliographic record
Abstract
PURPOSE: The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE. PATIENTS AND METHODS: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively. We selected up to 24 variables including sex, age, education duration, diabetes mellitus, and hypertension. We trained a machine-learning algorithm using TensorFlow based on the training dataset and then calculated its accuracy using the test dataset. The cost was calculated by conducting a logistic regression. RESULTS: The accuracy of the model in predicting CI based on the KDSQ only, the MMSE only, and the combination of the KDSQ and MMSE was 84.3%, 88.3%, and 86.3%, respectively. For the KDSQ, the sensitivity for detecting CI was 91.50% and the specificity for detecting normal cognition (NL) was 59.60%. The sensitivity of the MMSE was 94.35%, and the specificity was 59.62%. When combining the KDSQ and the MMSE, the sensitivity for detecting CI was 91.5% and the specificity for detecting NL was 61.5%. CONCLUSION: The algorithm predicting CI based on the MMSE is superior. However, the KDSQ can be administered more easily in clinical practice and the algorithm using KDSQ is a comparable screening tool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".