A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory.

By Leroy Hood, Mauricio Flores. 2012

534 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Economics and Econometrics, Health, Toxicology and Mutagenesis

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Arvid Lundervold
2. Daniel A. Beard
3. Valerio Persico
4. Antonio Pescapè
5. Giuseppe Aceto

In Economics and Econometrics

1. Arvid Lundervold
2. Valerio Persico
3. Antonio Pescapè
4. Giuseppe Aceto

In Health, Toxicology and Mutagenesis

1. Arvid Lundervold
2. Valerio Persico
3. Antonio Pescapè
4. Giuseppe Aceto

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “The access to bio-sensors and (edge) computing on wearable devices for monitoring disease or lifestyle, plus an ecosystem of machine learning and other computational medicine-based technologies, will then likely facilitate the transition to a new medical paradigm that is predictive, preventive, pers”
• Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0 (2020, background): “paradigm [24, 25, 98, 99, 100], that heavily relies on the genetic information of each individual.”
• Big Data Analytics in Healthcare (2015, background): “Liebeskind and Feldmann explored advances in neurovascular imaging and the role of multimodal CT or MRI including angiography and perfusion imaging on evaluating the brain vascular disorder and achieving precision medicine [33].”
• From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Initially, there were great expectations for omics data to provide clues on the mechanisms underlying disease initiation and progression as well as new strategies for disease prediction, prevention and treatment [1].”
• Harnessing next-generation informatics for personalizing medicine: a report from AMIA's 2014 Health Policy Invitational Meeting (2016, background): “Indeed, the emergence of “P4 Medicine” embraces the breadth of interpretations by defining a model of health care that is predictive, personalized, preventive, and participatory.(5) While it has always been a care provider’s primary goal to adjust treatment based on the specific characteristics of a”
• The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges (2018, methodology): “Based on a comprehensive understanding of an individual’s own biology, P4 Medicine is going to tailor treatments to the individual characteristics of the patient, in contrast to the current approach of clustering patients into treatment groups according to their phenotype.”