A theoretic framework for intelligent expert systems in medical encounter evaluation
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
Abstract: This paper describes a novel approach to implementation of a medical diagnosis expert system that can assist physicians with their daily practices. Differential artificial intelligence techniques are incorporated into a multi‐stage expert system to best represent the various phases of the patient diagnosis process. A weighted scoring system is used to represent the subjective analysis stage, while a rule‐based fuzzy expert system is employed to both interpret laboratory tests and imaging findings and suggest the final diagnosis. A model of various patient flow scenarios is presented to demonstrate the functionality of the proposed expert system. An actual example of patient walkthrough is used to demonstrate various computation steps from recording the patient chief complaint to arriving at the final diagnosis. It is shown that the conclusion arrived at by using the proposed system is consistent with a common diagnosis of a third party specialist who is asked to evaluate the performance of the system.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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