Additional file 1 of Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
Bibliographic record
Abstract
Additional file 1: Figure S1. An overview of the Long Short Term Memory (LSTM) model. Figure S2. Training loss of the Mini Mental State Examination (MMSE) Long Short Term Memory (LSTM) model. Figure S3. Change of Mini Mental State Examination (MMSE) score over time when the recommendation drugs were 3 acetylcholinesterase inhibitors (AChEIs; donepezil, rivastigmine and galantamine) only. Figure S4. Change of Mini Mental State Examination (MMSE) score over time when a medication was prescribed on a randomly selected visit. Figure S5. Change of Mini Mental State Examination (MMSE) score over time when recommendations were randomly shuffled. Figure S6. Change of Mini Mental State Examination (MMSE) score over time when ridge regression, random forest and one-dimensional Convolutional Neural Network (1D CNN) were used for drug recommendation, compared to the Long Short Term Memory (LSTM) model. Figure S7. Change of Mini Mental State Examination (MMSE) score over time when the Long Short Term Memory (LSTM) model was trained on the oversampled data. Figure S8. Change of Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) score over time when recommendations were given by a single Long Short Term Memory (LSTM) model developed using multitask learning. Figure S9. Change of Mini Mental State Examination (MMSE) score over time when the Long Short Term Memory (LSTM) model was trained with fewer observations per patient. Figure S10. Permutation feature importance of the Long Short Term Memory (LSTM) model. Table S1. Validation of the natural language processing (NLP) model performance on the UK Clinical Record Interactive Search (CRIS) data. Table S2. Validation of the natural language processing (NLP) performance on additional categories. Table S3. Implementation details of the Long Short Term Memory (LSTM) model. Table S4. The ratio of prescribed medications. Table S5. Quantified Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) score changes when Long Short Term Memory (LSTM) model was used for drug recommendation. Table S6. Quantified Mini Mental State Examination (MMSE) score changes when the recommendation drugs were 3 acetylcholinesterase inhibitors (AChEIs; donepezil, rivastigmine and galantamine) only. Table S7. Quantified Mini Mental State Examination (MMSE) score changes when a medication was prescribed on a randomly selected visit. Table S8. Quantified Mini Mental State Examination (MMSE) score changes when recommendations were randomly shuffled. Table S9. Implementation details of the ridge regression, random forest and one-dimensional Convolutional Neural Network (1D CNN). Table S10. Quantified Mini Mental State Examination (MMSE) score changes when ridge regression, random forest and one-dimensional Convolutional Neural Network (1D CNN) were used for drug recommendation. Table S11. Implementation details of the Long Short Term Memory (LSTM) model for the oversampled data. Table S12. Quantified Mini Mental State Examination (MMSE) score changes when the Long Short Term Memory (LSTM) model was trained on the oversampled data. Table S13. Implementation details of the Long Short Term Memory (LSTM) model for multitask learning. Table S14. Quantified Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) score changes when recommendations were given by a single Long Short Term Memory (LSTM) model developed using multitask learning. Table S15. Implementation details of the Long Short Term Memory (LSTM) model when the model was trained with fewer observations per patient. Table S16. Quantified Mini Mental State Examination (MMSE) score changes when the Long Short Term Memory (LSTM) model was trained with fewer observations per patient. Table S17. Permutation feature importance scales of the Long Short Term Memory (LSTM) model.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.634 | 0.094 |
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".