Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

Abstract

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Keywords: disposition; machine learning; meningioma; outcomes; predictive modeling.

Fig. 1

Receiver-operating characteristic curve for the ensemble model.

Fig. 2

Permutation importance demonstrates the relative importance of the top five most impactful variables for the ensemble model. The most important variable is assigned the value of “1.0” and all other variables are assigned the numerical values based on their importance relative to the most important variable. BMI, body mass index; ED, emergency department.

Fig. 3

Deeper analysis of each of the five most impactful variables. All X -axes represent probability of discharge to home, with 1 equivalent to 100% likelihood of discharge to home and 0 equivalent to 0% likelihood of discharge to home, and corresponds to lines. Panel (A) tumor size, with tumor size in mm along the Y -axis; Panel (B): BMI, with BMI groupings along the Y -axis; Panel (C) presentation at the ED; Panel (D) convexity tumor location; Panel (E) preoperative motor deficit; and Panel (F) legend. BMI, body mass index; ED, emergency department.

Fig. 4

Partial dependence for skull base and other tumor locations. Both X -axes represent probability of discharge to home, with 1 equivalent to 100% likelihood of discharge to home and 0 equivalent to 0% likelihood of discharge to home. Panel (A) skull base tumor location; Panel (B) other tumor location.