Computer methods and programs in biomedicine · 2025
An AI-based microsimulation for predicting health outcomes among people experiencing homelessness.
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Blasco-Calafat Antonio, Blanes-Selva Vicent, Doñate-Martínez Ascensión, Fragner Tobias, Alhambra-Borrás Tamara, Gawronska Julia, Moudatsou Maria, Tabaki Ioanna, Belogianni Katerina, Karnaki Pania, Varadé Miguel Rico, Trenado Rosa Gómez, Barrio-Cortes Jaime, Smith Lee, Gil-Salmeron Alejandro, Grabovac Igor, García-Gómez Juan M
Аннотация
BACKGROUND AND OBJECTIVE: People experiencing homelessness (PEH) face higher cancer risk due to social exclusion, housing and limited access to healthcare. This study proposes a microsimulation model using machine learning (ML) to predict the effect of quality of life, healthcare utilisation and empowerment at the end of the intervention under the Health Navigator Model, enabling cost-effective resource allocation and identifying high-risk subgroups.
MATERIALS & METHODS: We used data from 652 PEH recruited in four European countries (June 2022 - November 2023); 255 completed an 18-month Health Navigator Model programme. Standardised questionnaires were administered at baseline, four weeks and post-intervention. A modular ML microsimulation was built that (1) creates a constraint-based synthetic cohort, (2) estimates outcome changes by matching each simulated case to real program completers, and (3) sums those differences to gauge the intervention's impact. Multiple ML techniques were tested to keep the synthetic sample true to the original and to improve effect-size predictions.
RESULTS: CTGAN generated the most realistic synthetic baseline (propensity score = 0.152; 95 % CI 0.148-0.162), markedly outperforming univariant, multivariant and SMOTE approaches (> 0.21). Regression models reproduced most numerical outcomes with good fidelity (e.g., EQ-5D-5L MAE = 0.10 on a 0-1 scale; Health-Rating MAE = 10 on a 0-100 scale), while categorical outcomes were predicted within roughly one category. Binary classifiers yielded F1-scores of 0.58 for smoking status and 0.64 for programme adherence. An online demonstrator (https://epione.upv.es) visualises the process.
CONCLUSION: The proposed ML-based microsimulation generates realistic PEH profiles and projects intervention outcomes, providing a flexible, evidence-driven tool to optimise cancer-prevention strategies for PEH supporting evidence-based decision-making and optimise resource allocation, enhancing intervention outcomes by predicting the intervention before implementation.
MATERIALS & METHODS: We used data from 652 PEH recruited in four European countries (June 2022 - November 2023); 255 completed an 18-month Health Navigator Model programme. Standardised questionnaires were administered at baseline, four weeks and post-intervention. A modular ML microsimulation was built that (1) creates a constraint-based synthetic cohort, (2) estimates outcome changes by matching each simulated case to real program completers, and (3) sums those differences to gauge the intervention's impact. Multiple ML techniques were tested to keep the synthetic sample true to the original and to improve effect-size predictions.
RESULTS: CTGAN generated the most realistic synthetic baseline (propensity score = 0.152; 95 % CI 0.148-0.162), markedly outperforming univariant, multivariant and SMOTE approaches (> 0.21). Regression models reproduced most numerical outcomes with good fidelity (e.g., EQ-5D-5L MAE = 0.10 on a 0-1 scale; Health-Rating MAE = 10 on a 0-100 scale), while categorical outcomes were predicted within roughly one category. Binary classifiers yielded F1-scores of 0.58 for smoking status and 0.64 for programme adherence. An online demonstrator (https://epione.upv.es) visualises the process.
CONCLUSION: The proposed ML-based microsimulation generates realistic PEH profiles and projects intervention outcomes, providing a flexible, evidence-driven tool to optimise cancer-prevention strategies for PEH supporting evidence-based decision-making and optimise resource allocation, enhancing intervention outcomes by predicting the intervention before implementation.