Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson’s Disease
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Le résumé fourni par la source
One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson's Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson’s Disease
- Date Crossref
- 30/10/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Boston University pays non établi dans la noticeUniversité ou école supérieure
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Tufts University Center for Applied Brain and Cognitive Sciences pays non établi dans la noticeUniversité ou école supérieure
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Combat Capabilities Development Command Soldier Center pays non établi dans la noticeOrganisme public
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UMass Memorial Health Care pays non établi dans la noticeÉtablissement de santé
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UMass Memorial Medical Center pays non établi dans la noticeÉtablissement de santé
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Mayo Clinic in Arizona pays non établi dans la noticeÉtablissement de santé
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University of Illinois Chicago pays non établi dans la noticeUniversité ou école supérieure
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Spaulding Rehabilitation Hospital pays non établi dans la noticeÉtablissement de santé
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Harvard–MIT Division of Health Sciences and Technology pays non établi dans la noticeUniversité ou école supérieure
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Highland Instruments pays non établi dans la noticeInstitution
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U.S. Army DEVCOM Soldier Center pays non établi dans la noticeInstitution
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Department of Neurology pays non établi dans la noticeInstitution
Boston University, Center for Applied Brain and Cognitive Sciences — Tufts University et Combat Capabilities Development Command Soldier Center, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.