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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2509.12227 |
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| _version_ | 1866916975801794560 |
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| author | Ajirak, Marzieh Bein, Oded Bowen, Ellen Rose Kanellopoulos, Dora Falk, Avital Gunning, Faith M. Solomonov, Nili Grosenick, Logan |
| author_facet | Ajirak, Marzieh Bein, Oded Bowen, Ellen Rose Kanellopoulos, Dora Falk, Avital Gunning, Faith M. Solomonov, Nili Grosenick, Logan |
| contents | We propose a unified framework for adaptive routing in multitask, multimodal prediction settings where data heterogeneity and task interactions vary across samples. Motivated by applications in psychotherapy where structured assessments and unstructured clinician notes coexist with partially missing data and correlated outcomes, we introduce a routing-based architecture that dynamically selects modality processing pathways and task-sharing strategies on a per-sample basis. Our model defines multiple modality paths, including raw and fused representations of text and numeric features and learns to route each input through the most informative expert combination. Task-specific predictions are produced by shared or independent heads depending on the routing decision, and the entire system is trained end-to-end. We evaluate the model on both synthetic data and real-world psychotherapy notes predicting depression and anxiety outcomes. Our experiments show that our method consistently outperforms fixed multitask or single-task baselines, and that the learned routing policy provides interpretable insights into modality relevance and task structure. This addresses critical challenges in personalized healthcare by enabling per-subject adaptive information processing that accounts for data heterogeneity and task correlations. Applied to psychotherapy, this framework could improve mental health outcomes, enhance treatment assignment precision, and increase clinical cost-effectiveness through personalized intervention strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction Ajirak, Marzieh Bein, Oded Bowen, Ellen Rose Kanellopoulos, Dora Falk, Avital Gunning, Faith M. Solomonov, Nili Grosenick, Logan Machine Learning Artificial Intelligence We propose a unified framework for adaptive routing in multitask, multimodal prediction settings where data heterogeneity and task interactions vary across samples. Motivated by applications in psychotherapy where structured assessments and unstructured clinician notes coexist with partially missing data and correlated outcomes, we introduce a routing-based architecture that dynamically selects modality processing pathways and task-sharing strategies on a per-sample basis. Our model defines multiple modality paths, including raw and fused representations of text and numeric features and learns to route each input through the most informative expert combination. Task-specific predictions are produced by shared or independent heads depending on the routing decision, and the entire system is trained end-to-end. We evaluate the model on both synthetic data and real-world psychotherapy notes predicting depression and anxiety outcomes. Our experiments show that our method consistently outperforms fixed multitask or single-task baselines, and that the learned routing policy provides interpretable insights into modality relevance and task structure. This addresses critical challenges in personalized healthcare by enabling per-subject adaptive information processing that accounts for data heterogeneity and task correlations. Applied to psychotherapy, this framework could improve mental health outcomes, enhance treatment assignment precision, and increase clinical cost-effectiveness through personalized intervention strategies. |
| title | Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12227 |