An internet-based fuzzy logic expert system for organ transplantation assignment
Bibliographic record
Abstract
Organ transplantation is lifesaving. However, the demand for organs far exceeds supply. Due to the resource scarcity (i.e. organs), the special medical requirements for organ transplanting and the urgency in getting the organ to the recipient, it is critical to use information technology to coordinate the organ procurement and transplanting process and to allocate donated organs to recipients quickly, fairly and effectively. In this paper we analyse how information technology can be used to improve organ transplantation services and propose the use of an internet-based fuzzy logic expert system to assist physicians in solving the multi-criteria kidney allocation problem. A pilot fuzzy logic expert system for kidney allocation was developed and evaluated in comparison with two existing allocation algorithms a priority sorting system used by the Multiple Organ Retrieval and Exchange (MORE) program in Ontario, Canada and a point scoring systems used by the United Network for Organ Sharing (UNOS) in the USA. Our simulated experiment based on real patient data confirms that the fuzzy logic system can represent the expert's thinking in a satisfactory manner in handling complex trade-offs and overall, the fuzzy logic derived recommendations were more acceptable to the expert than those from the MORE and UNOS algorithms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".