A Revolução Silenciosa do Big Data em Medicina
DOI:
https://doi.org/10.24950/rspmi/Perspective/2017Palavras-chave:
Conjuntos de Dados, Medicina Baseada em Evidência, Registos de Saúde ElectrónicosResumo
.
Downloads
Referências
Data is giving rise to a new economy. The Economist [Internet]. [accessed 2017 Aug 28]. Available from: https://www.economist.com/news/briefing/21721634-how-it-shaping-up-data-giving-rise-new-economy
Provost F, Fawcett T. Data science and its relationship to big data and data-driven deci-sion making. Big Data. 2013r;1:51–9.
Shanafelt TD, Dyrbye LN, Sinsky C, Hasan O, Satele D, Sloan J, et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc. 2016 ;91:836–48.
Meystre SM, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann CU. Clinical data reuse or secondary use: current status and potential future progress. Yearb Med Inform. 2017;26.
Sim I. Two ways of knowing: big data and evidence-based medicine. Ann Intern Med. 2016;164:562–3.
Cano I, Teny A, Vela E, Miralles F, Roca J. Perspectives on Big Data applications of health information. Curr Opin Syst Biol. E2017;3:1–13.
Scott PJ, Rigby M, Ammenwerth E, Brender McNair J, Georgiou A, Hyppönen H, et al. Evaluation Considerations for Secondary Uses of
Clinical Data: Principles for an Evidence-based Approach to Policy and Implementation of Secondary Analysis. A Posi-tion Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group. Yearb Med Inform. 2017;26.
Topol E. Digital medicine: empowering both patients and clinicians. Lancet. 2016;388:740–1.
Piwek L, Ellis DA, Andrews S, Joinson A. The rise of consumer health wearables: promises and barriers. PLoS Med. 2016;13:e1001953–9.
Deo RC. Machine learning in medicine. Circulation. 2015;132:1920–30.
Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, et al. Phe-nomapping for novel classification of heart failure with preserved ejection fraction. Cir-culation. 2015;131:269–79.
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Devel-opment and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316:2402–10.
Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med. 2016;375:1216–9.
Blumenthal D. Realizing the value (and profitability) of digital health data. Ann Intern Med. 2017;166:842–3.
Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations. N Engl J Med. 2017;376:2507–9.
Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017; 318:517-8.
Fraccaro P, Sullivan DO, Plastiras P, Sullivan HO, Dentone C, Di Biagio A, et al. Behind the screens - Clinical decision support methodologies – A review. Health Policy and Technology. 2014;4:1–10.~
Gordon WJ, Fairhall A, Landman A. Threats to Information security - public health implications. N Engl J Med. 2017;377:707–9.
Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T, Hunter NL, et al. Real-world evidence - what is it and what can it tell us? N Engl J Med. 2016;375:2293–7.
Bierman AS, Tinetti ME. Precision medicine to precision care: managing multimorbidity. Lancet. 2016;388:2721–3
Ficheiros Adicionais
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2017 Medicina Interna
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
Direitos de Autor (c) 2023 Medicina Interna
Acesso livre
