Abstract: This study develops an advanced multimodal AI framework to strengthen early risk assessment in critical care and support resilient healthcare delivery. Utilizing the MIMIC-III database, this research extracted structured variables and clinical notes from 26,829 adult patients. A text mining approach based on the BERTopic model was employed to generate topic embeddings from unstructured notes, which were subsequently integrated with 16 quantitative variables. Six machine learning models: Adaboost, Gradient Boosting, Support Vector Classification (SVC), Bagging, Logistic Regression, and MLP Classifier were trained to predict short-term and long-term mortality outcomes. Model performance was evaluated through AUROC, accuracy, recall, precision, and F1-score metrics. The results demonstrate that integrating topic embeddings with structured data significantly improved short-term risk prediction. The SVC model, in particular, achieved an AUROC of 0.9137 for predicting 2-day mortality. Critical predictors identified included the Glasgow Coma Scale, White Blood Cell Count, and text-derived topics related to cardiovascular and neurological conditions. The study is based on a single-center dataset, limiting generalizability. Additionally, only a subset of textual data sources was analyzed, and improvements in long-term risk prediction were relatively modest. These findings demonstrate how multimodal AI can significantly improve early risk assessment and enhance resilience in critical care decision-making. This research pioneers the integration of BERTopic-based text mining with machine learning models for clinical risk prediction, highlighting the value of multimodal data fusion in improving predictive accuracy and enriching medical informatics.

Shih-Wei Wu, Chengcheng Li, Te-Nien Chien and Yao-Yu Zhang. “Advanced Multimodal AI for Resilient Healthcare: Enhancing Early Risk Assessment in Critical Care”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170121

@article{Wu2026,
title = {Advanced Multimodal AI for Resilient Healthcare: Enhancing Early Risk Assessment in Critical Care},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170121},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170121},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {Shih-Wei Wu and Chengcheng Li and Te-Nien Chien and Yao-Yu Zhang}
}