Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator.
A user-friendly calculator predicts chemotherapy toxicity in colorectal cancer patients, helping doctors adjust treatment plans before serious side effects occur.
This study develops an interpretable machine learning model using XGBoost and SHAP to predict severe myelosuppression in colorectal cancer patients receiving chemotherapy. The model was validated retrospectively and deployed as a web-based clinical calculator.
What the study was
- Study design
- Retrospective ML model development
- Population
- Colorectal cancer patients receiving chemotherapy
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- Frontiers in oncology
Why it surfaced
Relevant to CBC/ML watchlist topic. Standard ML model development with web deployment but retrospective single-center design.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.