CV Lattes
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Birthplace: Brazil
Medical researcher dedicated to challenges in Public Health, Epidemiology, Health Economics and Population Health Management. Research focused on the development of tools for risk stratification, incentive modeling and health management for people with chronic diseases. Currently is a member of the Administrative Council of Instituto Aramari Apo (Clinical Research and Continuing Medical Education). Head of the Data Laboratory for Quality of Care and Outcome Research (LaDa:QCOR) at Clarity Healthcare Intelligence conducting studies in Artificial Intelligence and Population Health Management (grant FAPESP 2019/09068-3).Education: Post-doctorate at FCM/UNICAMP (2019-2020) with research related to Applied Health Policy, Big Data Analytics, Machine Learning and Health Economics. PhD in Medicine from the State University of Campinas (UNICAMP) (2015). MSc in Medicine at University of Brasília (UnB) (2011). Graduated in Medicine at UnB. Specialist in Community Medicine and Public Health (2015). Residency in Internal Medicine at the Hospital de Base do Distrito Federal (2017). Residency in Cardiology at UNICAMP (2019).Associate Editor of the journals: Atherosclerosis, Frontiers in Endocrinology, Frontiers in Genetics e PLOS Medicine. Reviewer of journals such as Annals of Internal Medicine, Journal of the American College of Cardiology, European Heart Journal, Atherosclerosis, European Journal of Preventive Cardiology, Clinical Epidemiology, Lipids, PlosOne, Arquivos Brasileiros de Cardiologia, etc.Advisor of the postgraduate programs in Medicine at UnB and the School of Health Sciences of the Federal District. Researcher at the Laboratory of Vascular Biology and Atherosclerosis at the University of Campinas (UNICAMP) since 2015.Long-term experience in steering prospective observational studies and randomized clinical trials. Special interest in public health, epidemiology, applied statistics, machine learning and economic analysis in healthcare. Handles statistical packages and languages such as SPSS, Stata, R and Python. (Source: Lattes Curriculum)
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Clarity has developed a set of predictive models for health risks and costs. The models help to estimate the risk of diseases such as diabetes, hypertension, and hospitalizations for cardiovascular and non-cardiovascular causes in populations with acute and chronic diseases, as well as community populations (where there are people with and without diseases at baseline). Predictive health …
Faced with limited resource and the brutal dissipation of health-related expenditures (> 20 per cent of the volume of resources), population health management methods have become worldwide popular and are at the forefront of sustainable health care. Using predictive models for future costs based on clinical data, it is possible to identify the highest risk individuals and trigger intensiv…
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