Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets
Bibliographic record
Abstract
The realization of the Web as a common platform, medium, and interface for supporting human activities has attracted many researchers to the study of Web-based support systems (WSS). An important branch of WSS is Web-based decision support systems that provide intelligent support for making effective decisions in different domains. We focus on decision making in Web-based medical decision support systems (WMDSS). Uncertainty is a critical factor that affects decision making and reasoning in the medical field. A three-way decision-making approach is an effective and better choice to lessen the effects of uncertainty. It provides the provision for delaying certain or definite decisions in situations that lack sufficient evidence or accurate information in reaching certain conclusions. Particularly, the option of deferment decisions is added in this approach that provides the flexibility to further examine and investigate the uncertain and doubtful cases. The game-theoretic rough set (GTRS) model is a recent development in rough sets that can be used to determine the three rough set regions in the probabilistic rough sets framework by determining a pair of thresholds. The three regions are used to obtain three-way decision rules in the form of acceptance, rejection, and deferment rules. In this paper, we extend the GTRS model to analyze uncertainty involved in medical decision making. Experimental results with a GTRS-based approach on different health care datasets suggest that the approach may improve the overall quality of decision making in the medical field, as well as other fields. It is hoped that the incorporation of a GTRS component in WMDSS will enrich and enhance its decision-making capabilities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".