Empowering Health in Aging Populations: A Multimodal Vulnerability Tool for Frail Patients

Joanna G. Kondylis, Houman Javedan, Dimitris Bertsimas, Bharti Khurana
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:605-628, 2026.

Abstract

Frailty is a powerful predictor of adverse outcomes in older adults, yet its routine assessment remains limited in acute care settings due to the labor-intensive nature of the clinical Frailty Index ({FI}) scoring, requiring geriatric specialists and meticulous clinical assessment. We developed and externally validated the first automated multimodal vulnerability tool that provides a real-time risk assessment, integrating structured {EHR} data, clinical narratives, and {CT} imaging. Using data from two major Boston hospitals in the Mass General Brigham system, we trained models to predict six outcomes: 3- and 6-month all-cause mortality, 3- and 6-month hospital readmission, 6-month fall risk, and 1-year recurrent fall risk. Our multimodal approach achieved {AUC}s of 0.74–0.86, with improvements of up to 4.3% over single-modality models and 8–49% over {FI}’s predictive power. Beyond outcome prediction, we also sought to mirror clinical practice, where discrete frailty levels guide care planning. To this end, we developed a four-tier stratification system using k-means clustering and Optimal Policy Trees. This produces interpretable decision rules that assign patients to Non-, Pre-, Moderately-, and Severely- Vulnerable categories, actionable classifications that directly inform interventions, from fall prevention to advance care planning, while adding significantly to the prognostic ability of frailty assessments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-kondylis26a, title = {Empowering Health in Aging Populations: A Multimodal Vulnerability Tool for Frail Patients}, author = {Kondylis, Joanna G. and Javedan, Houman and Bertsimas, Dimitris and Khurana, Bharti}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {605--628}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/kondylis26a/kondylis26a.pdf}, url = {https://proceedings.mlr.press/v297/kondylis26a.html}, abstract = {Frailty is a powerful predictor of adverse outcomes in older adults, yet its routine assessment remains limited in acute care settings due to the labor-intensive nature of the clinical Frailty Index ({FI}) scoring, requiring geriatric specialists and meticulous clinical assessment. We developed and externally validated the first automated multimodal vulnerability tool that provides a real-time risk assessment, integrating structured {EHR} data, clinical narratives, and {CT} imaging. Using data from two major Boston hospitals in the Mass General Brigham system, we trained models to predict six outcomes: 3- and 6-month all-cause mortality, 3- and 6-month hospital readmission, 6-month fall risk, and 1-year recurrent fall risk. Our multimodal approach achieved {AUC}s of 0.74–0.86, with improvements of up to 4.3% over single-modality models and 8–49% over {FI}’s predictive power. Beyond outcome prediction, we also sought to mirror clinical practice, where discrete frailty levels guide care planning. To this end, we developed a four-tier stratification system using k-means clustering and Optimal Policy Trees. This produces interpretable decision rules that assign patients to Non-, Pre-, Moderately-, and Severely- Vulnerable categories, actionable classifications that directly inform interventions, from fall prevention to advance care planning, while adding significantly to the prognostic ability of frailty assessments.} }
Endnote
%0 Conference Paper %T Empowering Health in Aging Populations: A Multimodal Vulnerability Tool for Frail Patients %A Joanna G. Kondylis %A Houman Javedan %A Dimitris Bertsimas %A Bharti Khurana %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-kondylis26a %I PMLR %P 605--628 %U https://proceedings.mlr.press/v297/kondylis26a.html %V 297 %X Frailty is a powerful predictor of adverse outcomes in older adults, yet its routine assessment remains limited in acute care settings due to the labor-intensive nature of the clinical Frailty Index ({FI}) scoring, requiring geriatric specialists and meticulous clinical assessment. We developed and externally validated the first automated multimodal vulnerability tool that provides a real-time risk assessment, integrating structured {EHR} data, clinical narratives, and {CT} imaging. Using data from two major Boston hospitals in the Mass General Brigham system, we trained models to predict six outcomes: 3- and 6-month all-cause mortality, 3- and 6-month hospital readmission, 6-month fall risk, and 1-year recurrent fall risk. Our multimodal approach achieved {AUC}s of 0.74–0.86, with improvements of up to 4.3% over single-modality models and 8–49% over {FI}’s predictive power. Beyond outcome prediction, we also sought to mirror clinical practice, where discrete frailty levels guide care planning. To this end, we developed a four-tier stratification system using k-means clustering and Optimal Policy Trees. This produces interpretable decision rules that assign patients to Non-, Pre-, Moderately-, and Severely- Vulnerable categories, actionable classifications that directly inform interventions, from fall prevention to advance care planning, while adding significantly to the prognostic ability of frailty assessments.
APA
Kondylis, J.G., Javedan, H., Bertsimas, D. & Khurana, B.. (2026). Empowering Health in Aging Populations: A Multimodal Vulnerability Tool for Frail Patients. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:605-628 Available from https://proceedings.mlr.press/v297/kondylis26a.html.

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