An Exploration On-demand Article Recommender System for Cancer Patients Information Provisioning
Bibliographic record
Abstract
Information provision plays an important role in ed- ucating patients with serious illnesses, like cancer, to cope with their disease conditions and to actively partic- ipate in shared-decision making process. Recent stud- ies suggest that there is a lack of appropriate educa- tional resources for such patients, specifically prostate cancer patients. To address this issue, in this paper, a Knowledge-based Exploration on-demand article Rec- ommender System (called KERS) is proposed that can provide evidence-based information for patients. Rec- ognizing the fact that exploration is expensive when the user of the system is a human, the main idea in KERS is to minimize exploration while achieving the maximum long-term satisfaction. Therefore, using a knowledge- base developed by an expert in the field, KERS learns user interests as quickly as possible and then it ex- ploits this knowledge to recommend the best articles. Furthermore, KERS needs no information from users beforehand and it learns them through interacting with users. The system will help patients make informed de- cisions, and at the same time, will reduce the burden on the healthcare providers. The results of experiments have confirmed the effectiveness of the proposed system compared to baseline methods.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".