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The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.
Silva-Spínola, Anuschka; Baldeiras, Inês; Arrais, Joel P; Santana, Isabel.
Afiliación
  • Silva-Spínola A; Univ Coimbra, Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal.
  • Baldeiras I; Univ Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal.
  • Arrais JP; Univ Coimbra, Center for Innovative Biomedicine and Biotechnology, 3004-504 Coimbra, Portugal.
  • Santana I; Univ Coimbra, Faculty of Medicine, 3000-070 Coimbra, Portugal.
Biomedicines ; 10(2)2022 Jan 29.
Article en En | MEDLINE | ID: mdl-35203524
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer's disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease-patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biomedicines Año: 2022 Tipo del documento: Article País de afiliación: Portugal Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Biomedicines Año: 2022 Tipo del documento: Article País de afiliación: Portugal Pais de publicación: Suiza