Boosted-tree score
\[S(X)=\sum_{m=1}^{M}\eta f_m(X)\]
Each tree contributes a nonlinear correction; this is why XGBoost does not have one linear coefficient per laboratory variable.
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Computational XGBoost model
PenuX maps early laboratory data to a boosted-tree score and an estimated probability of later Atlanta-defined severe acute pancreatitis (SAP).
Research use only. The equations and feature importances are research outputs, not a bedside scoring rule.
Feature vector
\[X_t=[\mathrm{WBC},\mathrm{CRP},\mathrm{BUN},\mathrm{Cr},\mathrm{Glu},\mathrm{Ca},\mathrm{ALB},\mathrm{LDH},\mathrm{AMY},\mathrm{Hb},\mathrm{APTT},\mathrm{PT},\ldots]\]
Only measurements available at or before prediction time are eligible. Future organ-failure variables or post-deterioration interventions are excluded.
XGBoost
Probability
\[p_{raw}(X)=\sigma(S(X))=\frac{1}{1+e^{-S(X)}}\]
When calibration is enabled, a development-only mapping \(g\) gives \(p_{cal}=g(p_{raw})\).
High-sensitivity operating point
\[T_{watch}=\arg\max_T\mathrm{Specificity}(T)\quad\mathrm{s.t.}\quad\mathrm{Sensitivity}(T)\ge0.98\]
\[\mathrm{Alert}(X)=\mathbf{1}\{p_{cal}(X)\ge T_{watch}\}\]
The threshold is locked from out-of-fold development predictions and is not moved after inspection of the final test cohort.
Learned feature weights: XGBoost gain
The table below is loaded from model-weights.json, generated from the 1,289-record Guilin Multi-ML cohort. The source target is explicitly normalized from raw 0=SAP, 1=non-SAP to PenuX 1=SAP.
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total records
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development SAP prevalence
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scale_pos_weight
| Rank | Feature | Gain weight | Splits |
|---|---|---|---|
| Loading model weights… | |||
Do not interpret gain as a coefficient.
Gain is an unsigned measure of how much a feature improved tree objectives when it was used for splits. It cannot be multiplied by a raw lab value to reproduce the XGBoost probability. Direction and patient-specific contribution require SHAP/PDP or direct tree inference.
Outcome
\[Y=\begin{cases}1,&\text{later Atlanta-defined SAP}\\0,&\text{non-SAP}\end{cases}\]