Multicriteria Decision Analysis Methodology for Medical Diagnosis Aid
Bibliographic record
Abstract
The aim of this paper is to present the original methodology of acute leukemia diagnosis using a new classification procedure, called PROCTN. This procedure belongs to multicriteria decision analysis area and it is based on the scoring function to determine a subset of prototypes, which represent the closest resemblance with an object to be assigned. Then it applies the majority-voting rule to assign an object to a class. The implementation of PROCTN was carried out on cytological data of 108 cases of acute leukemia, using the classification rules of French, American and British hematologists, and was then applied on an independent test set of 83 cases of acute leukemia. Each case was defined by forty-seven parameters obtained by examining patient's bone marrow smears with optical microscope. In order to determine the percentage of correct classification of each subtype of acute leukemia, we have compared the results obtained by the procedure with the results given previously by the hematologist. 90 % of the cases were correctly classified by the proposed procedure. These primary results are satisfactory and show the efficiency of the new procedure. Although still an investigative method, the preliminary results are very encouraging and demonstrate the potential performances of this procedure for solving medical classification problems.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".