Meta-Bayesian Fusion Adaptation for Evacuation Prediction: A Hybrid XGBoost–Graph Neural Network Framework for the Mentawai Megathrust Scenario

Sularno Sularno (Department of Information System, Dharma Andalas University)
Wendi Boy (Department of Civil Engineering, Dharma Andalas University)
Putri Anggraini (Department of Information System, Dharma Andalas University)
Dio Prima Mulya (Department of Information System, Dharma Andalas University)
Rexcharles Enyinna Donatus (Africa Centre of Excellence on Technology Enhanced Learning, National Open University of Nigeria)

Jurnal Sistem Informasi dan Informatika
Vol 4 No 2 (2026): Juli 2026

Continuous monitoring of physiological conditions in intensive care units generates complex multivariate time-series data that can support early clinical decision-making. However, conventional hybrid models combining Long Short-Term Memory networks and XGBoost generally learn correlational patterns and may experience performance degradation under non-stationary conditions, sensor artifacts, and changes in patient characteristics. Moreover, these models are typically designed for outcome prediction rather than treatment-effect estimation. Therefore, a framework capable of integrating temporal forecasting, representation disentanglement, uncertainty estimation, and treatment-response analysis is required. Purpose: This study aims to develop a Causal Multi-Task Fusion Engine (CMTFE) for forecasting future vital-sign trajectories and estimating individualized treatment effects in IoT-supported ICU monitoring. Methods/Study Design/Approach: The proposed framework integrates a Temporal Convolutional Network for multivariate vital-sign encoding, a β-Variational Autoencoder for separating outcome-relevant and confounding representations, a Transformer decoder for probabilistic trajectory forecasting, and propensity-weighted XGBoost regressors for treatment-effect estimation. Experiments were conducted using 487,000 observation windows derived from 38,200 ICU stays in the MIMIC-IV dataset. The data were chronologically divided into training, validation, and testing sets at proportions of 70%, 15%, and 15%, respectively. Forecasting performance was evaluated using Mean Absolute Error, Root Mean Squared Error, and Continuous Ranked Probability Score. Treatment-effect estimation was evaluated on a semi-synthetic test set using Precision in Estimation of Heterogeneous Effect and Area Under the Receiver Operating Characteristic Curve. Results/Findings: CMTFE achieved an MAE of 0.281, RMSE of 0.390, and CRPS of 0.218, outperforming the evaluated LSTM, TCN, Transformer, LSTM–XGBoost, and TCN–XGBoost baselines. Under the constructed distribution-shift scenario, the model exhibited a relative MAE increase of 4.2%, compared with 9.5% for TCN–XGBoost. For semi-synthetic treatment-effect estimation, CMTFE obtained a PEHE of 0.187 and an AUC of 0.842. The ablation analysis further indicated that representation disentanglement, contrastive regularization, propensity-score weighting, and temporal convolution each contributed to the model’s performance. Novelty/Originality/Value: The principal contribution of this study lies in integrating temporal forecasting, disentangled representation learning, uncertainty quantification, and propensity-adjusted treatment-effect estimation within a unified ICU-monitoring framework. Unlike conventional hybrid models that combine neural and tree-based predictions through passive aggregation, CMTFE uses learned outcome-relevant representations to support both forecasting and intervention-response analysis. Nevertheless, the treatment-effect findings are based on semi-synthetic outcomes and should be interpreted as experimental decision-support evidence rather than validated clinical recommendations. External, prospective, and clinician-centered validation is required before real-world deployment.