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PRECISION ALS—an integrated pan European patient data platform for ALS.
- Source :
- Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration; Aug2023, Vol. 24 Issue 5/6, p389-393, 5p
- Publication Year :
- 2023
-
Abstract
- Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative condition. Despite significant advances in pre-clinical models that enhance understanding of disease pathobiology, translation of candidate drugs to effective human therapies has been disappointing. There is increasing recognition of the need for a precision medicine approach toward drug development, as many failures in translation can be attributed in part to disease heterogeneity in humans. PRECISION-ALS is an academic industry collaboration between clinicians, Computer Scientists, Information engineers, technologists, data scientists and industry partners that will address the key clinical, computational, data science and technology associated research questions to generate a sustainable precision medicine based approach toward new drug development. Using extant and prospectively collected population based clinical data across nine European sites, PRECISION-ALS provides a General Data Protection Regulation (GDPR) compliant framework that seamlessly collects, processes and analyses research-quality multimodal and multi-sourced clinical, patient and caregiver journey, digitally acquired data through remote monitoring, imaging, neuro-electric-signaling, genomic and biomarker datasets using machine learning and artificial intelligence. PRECISION-ALS represents a first-in-kind modular transferable pan-European ICT framework for ALS that can be easily adapted to other regions that face similar precision medicine related challenges in multimodal data collection and analysis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21678421
- Volume :
- 24
- Issue :
- 5/6
- Database :
- Complementary Index
- Journal :
- Amyotrophic Lateral Sclerosis & Frontotemporal Degeneration
- Publication Type :
- Academic Journal
- Accession number :
- 164784694
- Full Text :
- https://doi.org/10.1080/21678421.2023.2215838