The analysis from nonlinear distance metric to kernel-based prescription prediction system
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
The distance metric and its nonlinear variant play a substantial role in machine learning, particularly yoso in building kernel functions. Often, the Euclidean distance with a radial basis function (RBF) is used to construct a RBF kernel for nonlinear classification. However, domain implications periodically constrain the distance metrics. Specifically, within the domain of drug efficacy prediction, distance measures must account for time that varies based on disease duration, short to chronic. Recently, a distance-derived graph kernel approach was commercially licensed for drug prescription efficacy prediction. The analysis of the distance functions used therein, namely the Euclidean and cosine distance measures and their respective derived graph kernels, is provided. Theoretically, we provide a formulation of our efforts and demonstrate how both the Euclidean and cosine distance induce space and discuss the difference from geometric perspectives. The aforementioned approach is likewise empirically evaluated using a million-plus patient subset of a life-spanning, real-world, electronic health record database. Diseases are characterized as either short in duration or chronic and either common, hence balanced data, or relatively rare, hence imbalanced. Empirically, the system accurately predicted the efficacy of prescriptions for both balanced and imbalanced and short-term and chronic diseases, with at least one of the measures used being statistically significantly superior to conventional prediction methods. Succinctly, for short-term, balanced diseases, the Euclidean and cosine measures were generally statistically equivalent. For short-term, imbalanced diseases however, the Euclidean measure was superior to the cosine measure, at times and not infrequently, statistically significantly so. For chronic, balanced diseases, Euclidean was slightly superior to the cosine measure, but they were statistically equivalent. In contrast, for chronic, imbalanced diseases, the cosine measure was consistently statistically significantly superior to the Euclidean measure. These findings indicate the need for both measures depending on the use case. Our empirical findings match our theoretical underpinnings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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