Details
| Original language | English |
|---|---|
| Title of host publication | Data Science for Healthcare |
| Subtitle of host publication | Methodologies and Applications |
| Editors | Sergio Consoli, Diego Reforgiato Recupero, Milan Petković |
| Pages | 3-38 |
| Number of pages | 36 |
| Edition | 1. |
| ISBN (electronic) | 978-3-030-05249-2 |
| Publication status | Published - 24 Feb 2019 |
| Externally published | Yes |
Abstract
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.
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Medicine(all)
- General Medicine
Cite this
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Data Science for Healthcare: Methodologies and Applications. ed. / Sergio Consoli; Diego Reforgiato Recupero; Milan Petković. 1. ed. 2019. p. 3-38.
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research
}
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 - Lojo, Aizea
AU - Chatterjea, Supriyo
AU - Consoli, Sergio
AU - Despenic, Marija
AU - Heinrich, Adrienne
AU - Kustra, Jacek
AU - Petković, Milan
AU - Costa-Soria, Cristobal
AU - Czech, Paul
AU - Garattini, Chiara
AU - Hamelinck, Dirk
AU - Verachtert, WIlfried
AU - Wuyts, Roel
AU - Kraaij, Wessel
AU - Sanchez, Marga Martin
AU - Mayer, Miguel A.
AU - Melideo, Matteo
AU - Menasalvas Ruiz, Ernestina
AU - Rodriguez Gonzalez, Alejandro
AU - Aarestrup, Frank Moller
AU - Narro Artigot, Elvira
AU - Reforgiato Recupero, Diego
AU - Roesems Kerremans, Gisele
AU - Ruping, Stefan
AU - Sasaki, Felix
AU - Spek, Wouter
AU - Stojanovic, Nenad
AU - Vasiljevs, Andrejs
N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.
PY - 2019/2/24
Y1 - 2019/2/24
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 - Contribution to book/anthology
SN - 978-3-030-05248-5
SP - 3
EP - 38
BT - Data Science for Healthcare
A2 - Consoli, Sergio
A2 - Reforgiato Recupero, Diego
A2 - Petković, Milan
ER -