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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.18732 |
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| _version_ | 1866912290644688896 |
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| author | Weikert, Thomas Roellin, Eljas Pérez-Serrano, Monica Du, Elisa Heumos, Lukas Theis, Fabian J. Paez-Granados, Diego Awai, Chris Easthope |
| author_facet | Weikert, Thomas Roellin, Eljas Pérez-Serrano, Monica Du, Elisa Heumos, Lukas Theis, Fabian J. Paez-Granados, Diego Awai, Chris Easthope |
| contents | A significant and rising proportion of the global population suffer from non-communicable diseases, such as neurological disorders. Neurorehabilitation aims to restore function and independence of neurological patients through providing interdisciplinary therapeutic interventions. Computational neurorehabilitation, an automated simulation approach to dynamically optimize treatment effectivity, is a promising tool to ensure that each patient has the best therapy for their current status. However, computational neurorehabilitation relies on integrated data flows between clinical assessments, predictive models, and healthcare professionals. Current neurorehabilitation practice is limited by low levels of digitalization and low data interoperability. We here propose and demonstrate an embedded intelligent health system that enables detailed digital data collection in a modular fashion, real-time data flows between patients, models, and clinicians, clinical integration, and multi-context capacities, as required for computational neurorehabilitation approaches. We give an outlook on how modern exploratory data analysis tools can be integrated to facilitate model development and knowledge inference from secondary use of observational data this system collects. With this blueprint, we contribute towards the development of integrated computational neurorehabilitation workflows for clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18732 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embedding computational neurorehabilitation in clinical practice using a modular intelligent health system Weikert, Thomas Roellin, Eljas Pérez-Serrano, Monica Du, Elisa Heumos, Lukas Theis, Fabian J. Paez-Granados, Diego Awai, Chris Easthope Software Engineering A significant and rising proportion of the global population suffer from non-communicable diseases, such as neurological disorders. Neurorehabilitation aims to restore function and independence of neurological patients through providing interdisciplinary therapeutic interventions. Computational neurorehabilitation, an automated simulation approach to dynamically optimize treatment effectivity, is a promising tool to ensure that each patient has the best therapy for their current status. However, computational neurorehabilitation relies on integrated data flows between clinical assessments, predictive models, and healthcare professionals. Current neurorehabilitation practice is limited by low levels of digitalization and low data interoperability. We here propose and demonstrate an embedded intelligent health system that enables detailed digital data collection in a modular fashion, real-time data flows between patients, models, and clinicians, clinical integration, and multi-context capacities, as required for computational neurorehabilitation approaches. We give an outlook on how modern exploratory data analysis tools can be integrated to facilitate model development and knowledge inference from secondary use of observational data this system collects. With this blueprint, we contribute towards the development of integrated computational neurorehabilitation workflows for clinical practice. |
| title | Embedding computational neurorehabilitation in clinical practice using a modular intelligent health system |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.18732 |