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Paradigm changes for diagnosis: using big data for prediction.
Roca, Josep; Tenyi, Akos; Cano, Isaac.
Afiliação
  • Roca J; Hospital Clínic, IDIBAPS, Facultat de Medicina, Universitat de Barcelona, Barcelona, Catalunya, Spain.
  • Tenyi A; Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Av. Monforte de Lemos, 3-5. Pabellón 11. Planta 0, 28029, Madrid, Catalunya, Spain, Phone: +34-932275747, Fax: +34-932275455.
  • Cano I; Hospital Clínic, IDIBAPS, Facultat de Medicina, Universitat de Barcelona, Barcelona, Catalunya, Spain.
Clin Chem Lab Med ; 57(3): 317-327, 2019 02 25.
Article em En | MEDLINE | ID: mdl-30530879
ABSTRACT
Due to profound changes occurring in biomedical knowledge and in health systems worldwide, an entirely new health and social care scenario is emerging. Moreover, the enormous technological potential developed over the last years is increasingly influencing life sciences and driving changes toward personalized medicine and value-based healthcare. However, the current slow progression of adoption, limiting the generation of healthcare efficiencies through technological innovation, can be realistically overcome by fostering convergence between a systems medicine approach and the principles governing Integrated Care. Implicit with this strategy is the multidisciplinary active collaboration of all stakeholders involved in the change, namely citizens, professionals with different profiles, academia, policy makers, industry and payers. The article describes the key building blocks of an open and collaborative hub currently being developed in Catalonia (Spain) aiming at generation, deployment and evaluation of a personalized medicine program addressing highly prevalent chronic conditions that often show co-occurrence, namely cardiovascular disorders, chronic obstructive pulmonary disease, type 2 diabetes mellitus; metabolic syndrome and associated mental disturbances (anxiety-depression and altered behavioral patterns leading to unhealthy life styles).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atenção à Saúde / Medicina de Precisão / Big Data Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: Europa Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atenção à Saúde / Medicina de Precisão / Big Data Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País como assunto: Europa Idioma: En Ano de publicação: 2019 Tipo de documento: Article