XGBoost-based model for predicting PICC occlusion risk in cancer patients: Insights from SHAP analysis
Bibliographic record
Abstract
Peripherally inserted central catheters (PICC) are commonly used in cancer patients, but occlusion is a frequent complication. Early prediction of the occlusion risk can guide timely interventions and improve patient outcomes. This study develops and validates a machine-learning model to predict the PICC occlusion risk in cancer patients using clinical data from electronic medical records. In this retrospective, single-center study, data from cancer patients with PICC lines were analyzed. Three machine learning algorithms—logistic regression, random forest, and XGBoost—were used to predict the occlusion risk. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). Key risk factors, including patient demographics, clinical conditions, and catheter maintenance practices, were incorporated. XGBoost outperformed the other models, achieving AUC values of 0.909 in the training cohort and 0.759 in the validation cohort. Key predictors of PICC occlusion included catheter duration, electrolyte disturbances, the chemotherapy drug type, and the insertion length. SHAP analysis provided transparent model interpretation. The XGBoost model effectively predicts the PICC occlusion risk and identifies key predictors. While limited by its retrospective design, the study suggests the potential for clinical integration to improve patient outcomes. Further prospective studies are needed. Developed a machine learning model to predict PICC occlusion risk in cancer patients. XGBoost model demonstrated high predictive accuracy (AUC 0.909 in training, 0.759 in validation). Identified key predictors of PICC occlusion: catheter duration, chemotherapy regimen, electrolyte disturbances. SHAP analysis provided transparent insights into feature importance, aiding clinical decision-making. Statistical validation confirmed the significance of key risk factors, supporting personalized patient management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".