Predicting rehabilitation treatment helpfulness to stroke patients: A supervised learning approach
Bibliographic record
Abstract
Stroke (Cerebral vascular accident, CVA) is a common and serious disease. Most of the survivals would be disabled after their illness recovery, causes serious burden on caregivers. It is said that rehabilitation could help functional recovery of stroke patients, regain independence after stroke. Due to the long course of stroke, how to prevent survivals from recurrence is an important issue. This study attempts to examine the relationship between stroke recurrence and strength of rehabilitation, and build a stroke recurrence prediction model utilizing a number of supervised learning techniques to assist physicians with making clinical decisions.In the past, most of the related work used the samples from a single hospital as a sample, but it cannot fully catch all the clinic information of the patients. Therefore, this study used the Longitudinal Health Insurance Database 2010 of the NHIRD as the data source, to examine the effectiveness of rehabilitation.In terms of accuracy rate of all classifiers, we get the best effectiveness (78%) while adopting the inpatient admission dataset and C4.5 to predict recurrence. We also find physical therapy, occupational therapy and speech therapy treatments during inpatient admission are the key factors to decrease the chance to recrudesce in the rehabilitation periods. The higher strength and frequency rehabilitation treatment is also the key influence variables in our high accuracy prediction model which means that is useful to lower the recurrence rate of stroke patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".