Predicting perioperative outcomes from surgical data during one lung ventilation
Bibliographic record
Abstract
Patients routinely undergo surgeries executed under general anesthesia that require ventilation. Sometimes this mechanical ventilation is not only harmful to health but also a significant cause of death. One lung ventilation (OLV) assisted lung surgery is not free from those complications. During OLV assisted lung surgery, ventilation is applied to the lung opposite the side of the operation, and the other lung (where the surgery will take place) is already damaged. For these reasons, mechanical ventilation procedures may cause acute ventilator-induced lung injuries. These lung injuries can be the reason for different pulmonary and respiratory complications, which may lead to patient death. So it is essential to predict OLV assisted surgery-related outcomes to reduce negative results. I have used machine learning to predict the perioperative outcome. I have used a real-world dataset of OLV assisted lung surgeries collected in Manitoba. The dataset is the real-time olv assisted lung surgery data from 80 patients surgery information. Due to the uneven class distribution, I have used SMOTE for oversampling the data. I divided the dataset into three parts: i) Preoperative Data, ii) Intraoperative Data, and iii) Combined Data. Three different classification algorithms, Random Forests (RF), Support Vector Machines (SVM), and Logistic Regression (LR), have been applied to the different combinations of datasets. Using intra-operative data oversampled by SMOTE using the SVM classification algorithm gives the best accuracy with an F1-score of 0.70 and AUC of 0.61 for the prediction of surgical complication.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".