Big Data is impacting and will impact even more most sectors of our lifes. In particular, the healthcare industry could be revolutionized by Big Data analytics since it could improve its operational efficiencies, could help predict disease epidemics and plan responses, enhance the monitoring of clinical trials and in general optimize healthcare spending [1-2]. Starting from examples from orthodontic data [3-5], we will show how applying complex networks' analysis help to visualise, filter and mine complex datasets. We will discuss the general applicability of such approach to medical data and the possibility of implementing a medical doctors' oriented interface for designing and supporting virtual experiments. In such a way, we hope to enlarge and enhance the concept of "data driven medicine", furnishing instruments to let new medical knowledge emerge from Big Data. [1] A Look at Challenges and Opportunities of Big Data Analytics in Healthcare 2013 IEEE International Conference on Big Data Raghunath Nambiar, Adhiraaj Sethi, Ruchie Bhardwaj, Rajesh Vargheese [2] Better Health Care Through Data: How health analytics could contain costs and improve care By KATHY PRETZ 8 September 2014 http://theinstitute.ieee.org/technology-focus/technology-topic/better-health-care-through-data http://theinstitute.ieee.org/ns/quarterly_issues/tisep14.pdf [3] A network approach to orthodontic diagnosis Auconi, P., Caldarelli, G., Scala, A., Ierardo, G., & Polimeni, A. Orthodontics & Craniofacial Research,14(4), 189-197 (2011) [4] Using Networks To Understand Medical Data: The Case of Class III Malocclusions Antonio Scala , Pietro Auconi, Marco Scazzocchio, Guido Caldarelli, James A. McNamara, Lorenzo Franchi PLoS One 7-e44521 (2012) [5] Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony Antonio Scala , Pietro Auconi, Marco Scazzocchio, Guido Caldarelli, James A. McNamara, Lorenzo Franchi 2014 New J. Phys. 16 115017

Complex Networks for data-Driven Medicine

Antonio Scala
2016

Abstract

Big Data is impacting and will impact even more most sectors of our lifes. In particular, the healthcare industry could be revolutionized by Big Data analytics since it could improve its operational efficiencies, could help predict disease epidemics and plan responses, enhance the monitoring of clinical trials and in general optimize healthcare spending [1-2]. Starting from examples from orthodontic data [3-5], we will show how applying complex networks' analysis help to visualise, filter and mine complex datasets. We will discuss the general applicability of such approach to medical data and the possibility of implementing a medical doctors' oriented interface for designing and supporting virtual experiments. In such a way, we hope to enlarge and enhance the concept of "data driven medicine", furnishing instruments to let new medical knowledge emerge from Big Data. [1] A Look at Challenges and Opportunities of Big Data Analytics in Healthcare 2013 IEEE International Conference on Big Data Raghunath Nambiar, Adhiraaj Sethi, Ruchie Bhardwaj, Rajesh Vargheese [2] Better Health Care Through Data: How health analytics could contain costs and improve care By KATHY PRETZ 8 September 2014 http://theinstitute.ieee.org/technology-focus/technology-topic/better-health-care-through-data http://theinstitute.ieee.org/ns/quarterly_issues/tisep14.pdf [3] A network approach to orthodontic diagnosis Auconi, P., Caldarelli, G., Scala, A., Ierardo, G., & Polimeni, A. Orthodontics & Craniofacial Research,14(4), 189-197 (2011) [4] Using Networks To Understand Medical Data: The Case of Class III Malocclusions Antonio Scala , Pietro Auconi, Marco Scazzocchio, Guido Caldarelli, James A. McNamara, Lorenzo Franchi PLoS One 7-e44521 (2012) [5] Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony Antonio Scala , Pietro Auconi, Marco Scazzocchio, Guido Caldarelli, James A. McNamara, Lorenzo Franchi 2014 New J. Phys. 16 115017
2016
Istituto dei Sistemi Complessi - ISC
Complex Networks
Data Driven Medicine
Interdisciplinary Studies
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/316423
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