Prediction of drug amount in Parkinson's disease using hybrid machine learning systems and radiomics features
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Parkinson's disease (PD) is progressive and heterogeneous. Levodopa is widely prescribed to control PD, and its long‐term‐treatment leads to dyskinesia in a dose‐dependent manner. Interpretation of clinical trials comparing different drug treatments for PD is complicated by different dose intensities employed: higher doses of levodopa produce better symptomatic control but more late complications. Thus, the dose must be recalibrated and reduced gradually. Since recommendations for gradually reducing Levodopa are currently lacking and estimation of Levodopa amount can help doctor to correctly prescribe drug amount, this study aims to predict Levodopa amount and incremental doses using Hybrid Machine Learning Systems (HMLS) and a mixture of radiomics and clinical features. We selected 264 patients from PPMI and obtained 950 features including imaging and nominating features. We generated seven datasets constructed from the dataset in years 0 and 1, which linked with outcomes, (O1) patients being on/off drug in year 1, (O2) dose amount in year 1, and (O3‐8) incremental dose from 1st to 2nd, 2nd to 3rd, 3rd to 4th, 4th to 5th, 1st to 4th, 1st to 5th year. HMLSs included 10 feature extraction/9 feature selection algorithms followed by 10 prediction algorithms. To predict O1, timeless dataset + Random Forest + ReliefA had the highest accuracy~88.5% ± 2.2%, and external testing~91.6%. Furthermore, to predict O2, timeless dataset + Minimum Redundancy Maximum Relevance Algorithm (MRMR) + K Nearest Neighbor Regressor (KNN‐R) achieved a mean absolute error (MAE) ~ 47.5 ± 13.6 ([30.3:850 milligram]) and external testing~31.9. To predict dose increments (O3‐8), HMLSs: Unsupervised Feature Selection with Ordinal Locality + KNNR, ReliefA + KNNR, ReliefA + KNNR, Local Learning‐based Clustering Feature Selection + KNNR, MRMR + KNNR, and MRMR + KNNR applied to timeless datasets resulted in MAEs ~ 0.42 ± 0.18, 0.10 ± 0.09, 0.04 ± 0.01, 0.24 ± 0.15, 0.25 ± 0.05, and 0.33 ± 0.26 ([0.23:29.7]), respectively. Moreover, their external testing confirmed our findings. We demonstrated that timeless datasets including a mixture of clinical and imaging features, linked with appropriate HMLSs, significantly improve prediction performances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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