MACHINE LEARNING TO PREDICT HOMEBOUND STATUS IN OLDER ADULTS USING CANADIAN LONGITUDINAL STUDY ON AGING DATASET
Bibliographic record
Abstract
Abstract Individuals who are unable to leave their home or with great difficulty are considered homebound or semi-homebound. Homebound status is strongly associated with disability, social isolation, healthcare use and costs, and mortality. Most homebound older adults have multiple chronic conditions and poor health. There is not enough information about homebounded older adults in Canada. The Comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA) presents an excellent opportunity to study the complex factors associated with homebound status and the interplay between physical, social, psychological, and environmental determinants over time. This is a population-level study which makes use of provincial healthcare registration data to sample older adults across the country. We obtained the first wave of CLSA dataset containing samples from 21667 individuals over 3223 variables. We developed a definition of ‘homeboundedness’ in using life-space index variables present in the CLSA datatset. The dataset contained numerical, categorical and missing values. After preprocessing, we selected 1101 Homebound and 20521 Non-Homebound individuals, and 1771 variables. We showed that Random Forest classifier (with missing values) provided an AUC ROC and PR of 0.89 and 0.49. The missing value imputation did not improve the results significantly. Using feature hashing, we converted the dataset to numerical values; the AUCs improved to 0.96 and 0.71 at the cost of losing interpretation. In future, we will consult clinical experts in choosing the relevant features and use analytical methods to select features and compare. We will also test these predictive models on the next wave of this dataset.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".