TY - CHAP
T1 - Data science in healthcare
T2 - benefits, challenges and opportunities
AU - Abedjan, Ziawasch
AU - Boujemaa, Nozha
AU - Campbell, Stuart
AU - Casla, Patricia
AU - Chatterjea, Supriyo
AU - Consoli, Sergio
AU - Costa-Soria, Cristobal
AU - Czech, Paul
AU - Despenic, Marija
AU - Garattini, Chiara
AU - Hamelinck, Dirk
AU - Heinrich, Adrienne
AU - Kraaij, Wessel
AU - Kustra, Jacek
AU - Lojo, Aizea
AU - Sanchez, Marga Martin
AU - Mayer, Miguel A.
AU - Melideo, Matteo
AU - Menasalvas, Ernestina
AU - Aarestrup, Frank Moller
AU - Artigot, Elvira Narro
AU - Petković, Milan
AU - Recupero, Diego Reforgiato
AU - Gonzalez, Alejandro Rodriguez
AU - Kerremans, Gisele Roesems
AU - Roller, Roland
AU - Romao, Mario
AU - Ruping, Stefan
AU - Sasaki, Felix
AU - Spek, Wouter
AU - Stojanovic, Nenad
AU - Thoms, Jack
AU - Vasiljevs, Andrejs
AU - Verachtert, Wilfried
AU - Wuyts, Roel
PY - 2019/1/1
Y1 - 2019/1/1
N2 - The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain.
AB - The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain.
UR - http://www.scopus.com/inward/record.url?scp=85064376260&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05249-2_1
DO - 10.1007/978-3-030-05249-2_1
M3 - Chapter
SN - 978-3-030-05248-5
SP - 3
EP - 38
BT - Data Science for Healthcare
A2 - Consoli, S.
A2 - Reforgiato Recupero, D.
A2 - Petković, M.
PB - Springer
CY - Cham
ER -