Trend Estimation of Blood Glucose Level Fluctuations Based on Data Mining
Bibliographic record
Abstract
We have fabricated calorie-calculating software that calculates and records the total calorific food intake by choosing a meal menu selected using a computer mouse. The purpose of this software was to simplify data collection throughout a person's normal life, even if they were inexperienced computer operators. Three portable commercial devices have also been prepared a blood glucose monitor, a metabolic rate monitor and a mobile-computer, and linked into the calorie-calculating software. Time-course changes of the blood glucose level, metabolic rate and food intake were measured using these devices during a 3 month period. Based on the data collected in this study we could predict blood glucose levels of the next morning (FBG) by modeling using data mining. Although a large error rate was found for predicting the absolute value, conditions could be found that improved the accuracy of the predicting trends in blood glucose level fluctuations by up to 90 %. However, in order to further improve the accuracy of estimation it was necessary to obtain further details about the patients' life style or to optimise the input variables that were dependent on each patient rather than collecting data over longer periods.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".