Prioritization of congenital cardiac surgical patients using fuzzy reasoning – a solution to the problem of the waiting list?
Bibliographic record
Abstract
Surgical waiting lists are of high importance in countries, where the national health system is unable to deliver surgical services at a rate that would allow patients to avoid unnecessary periods of waiting. Prioritization of these lists, however, is frequently arbitrary and inconsistent. The objective of our research was to analyze the medical decision-making process when prioritizing patients with congenital cardiac malformations for cardiac surgical procedures, identifying an appropriate representation of knowledge, and transferring this knowledge onto the design and implementation of an expert system ("PrioHeart"). The medical decision-making process was stratified into three stages. The first was to analyze the details of the procedure and patient to define important impact factors on clinical priority, such as the risk of adverse events. The second step was to evaluate these impact factors to define an appropriate "timing category" within which a procedure should be performed. The third, and final, step was to re-evaluate the characteristics of individual patients to differentiate between those in the same timing category. We implemented this decision-making process using a rule-based production system with support for fuzzy sets, using the FuzzyCLIPS inference engine and expert system shell as a suitable development environment for the knowledge base. The "PrioHeart" expert system was developed to give paediatric cardiologists a tool to allow and facilitate the prioritization of patients on the cardiosurgical waiting list. Evaluation of "PrioHeart" on limited sets of patients documented appropriate results of prioritization, with a significant correlation between the prioritization made using "PrioHeart" and those results obtained by the individual consultant specialist. We conclude that our study has demonstrated the feasibility of using an expert system approach with a fuzzy, rule-based production system to implement the prioritization of cardiac surgical patients. The approach may potentially be transferable to other medical subspecialities.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".