Prediction of the progression of endometrial hyperplasia in women of premenopausal and menopausal age based on an analysis of clinical and anamnestic indicators using multiparametric neural network clustering
Bibliographic record
Abstract
Background.A number of studies are aimed at solving the problems of implementing innovative medical information technologies, but the issues of family medicine informatisation have not been fully resolved.This is especially important to optimise the diagnosis of the most common diseases.Objectives.The aim of our study was to develop a methodology for predicting the progression of endometrial hyperplasia at the primary healthcare level based on an analysis of clinical and anamnestic indicators using multiparameter neural network clustering.Material and methods.Clinical and anamnestic data was obtained based on the results of a retrospective analysis of the inpatient charts of 52 patients with non-atypical endometrial hyperplasia.For a deeper analysis and clustering, the neural network approach was used with the NeuroXL Classifier add-in application for Microsoft Excel.Results.The results of the cluster analysis showed that when predicting the progression of endometrial hyperplasia based on an analysis of clinical and anamnestic indicators, it is important to take into account the combination of the use of intrauterine contraception, as well as infertility, obesity and diseases of the gastrointestinal tract in patients.At the same time, the probability of progression of endometrial hyperplasia also increases with an increase in the number of operative obstetric and gynaecological interventions.Conclusions.In order to effectively and objectively assign patients to the risk group for the progression of endometrial hyperplasia according to the indicators obtained during observation, neural network clustering was used, which allows one to determine the value of combined changes of certain parameters for the prognosis of the progression of the disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".