Prediction of Patient's Adherence to the Post-Intubation Tracheal Stenosis Follow-up Plan in Iran: Application of two Data Mining Techniques.
Bibliographic record
Abstract
BACKGROUND: Timely diagnosis of post-intubation tracheal stenosis (PITS), which is one of the most serious complications of endotracheal intubation, may change its natural history. To prevent PITS, patients who are discharged from the intensive care unit (ICU) with more than 24 hours of intubation should be actively followed-up for three months after extubation. This study aimed to evaluate the abilities of artificial neural network (ANN) and decision tree (DT) methods in predicting the patients' adherence to the follow-up plan and revealing the knowledge behind PITS screening system development requirements. MATERIALS AND METHODS: In this cohort study, conducted in 14 ICUs during 12 months in ten cities of Iran, the data of 203 intubated ICU-discharged patients were collected. Ten influential factors were defined for adherences to the PITS follow-up (P<0.05). A feed-forward multilayer perceptron algorithm was applied using a training set (two-thirds of the entire data) to develop a model for predicting the patients' adherence to the follow-up plan three months after extubation. The same data were used to develop a C5.0 DT in MATLAB 2010a. The remaining one-third of data was used for model testing, based on the holdout method. RESULTS: The accuracy, sensitivity, and specificity of the developed ANN classifier were 83.30%, 72.70%, and 89.50%, respectively. The accuracy of the DT model with five nodes, 13 branches, and nine leaves (producing nine rules for active follow-up) was 75.36%. CONCLUSION: The developed classifier might aid care providers to identify possible cases of non-adherence to the follow-up and care plans. Overall, active follow-up of these patients may prevent the adverse consequences of PITS after ICU discharge.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".