Toward automated categorization of mobile health and fitness applications
Bibliographic record
Abstract
In recent years, with the explosive adoption of smart phone devices, mobile health and fitness applications have been increasingly used by healthcare practitioners and the general public to manage electronic health records, chronic medical conditions, dietary references etc. Despite the rapid growth in the number of mobile and fitness applications on various platforms, very little work has been done to quantitatively and qualitatively assess these applications to guide users in the selection process. Automatic categorization of mobile health and fitness applications is the first step in this direction. In this paper, we report results from crawling 1,430 Android and 62,286 iOS apps in Nov. 2013. Among them, 1,399 apps were manually classified to one or multiple categories out of a total of 11 categories. Text mining tools were applied to the description section of the apps for keyword extraction, feature selection and automatic categorization. The classifiers we experimented with have comparable performance with Linear SVC achieving the highest precision, recall and f1 scores of 0.89, 0.79 and 0.88, respectively.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".