Prediction of complications in spine surgery using machine learning: a Health 4.0 study on National Surgical Quality Improvement Program beyond logistic regression model
Bibliographic record
Abstract
With the advancement of the revolutionary artificial intelligence (AI) technologies, health-care services are rapidly moving toward an intelligent cyber physical system referred to as Health 4.0. In essence, the ability to predict surgical complications is all-important for both surgeons and patients. Recently, the use of machine learning (ML) algorithms for predicting complications has gained much attention. Even though many mature and reliable algorithms exist in the field of ML, the logistic regression (LR) algorithm has been the most widely used in complication prediction. In this study, we used the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database to compare the performance of LR to other ML algorithms for predicting complications during spine surgery. The database included 177 681 patients who underwent spine surgery. The occurrence of intraoperative morbidity was relatively low (9.4 per cent) in comparison to the total number of the dataset population, and hence, the dataset under study was considered imbalanced. To thoroughly evaluate and compare the proposed ML algorithms, the dataset was balanced and the algorithms were applied on both the balanced and imbalanced dataset. The results indicated that, in general, no significant difference was found between the performance of LR and random forest (RF), boosted tree (BT), and decision tree (DT).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".