Big data analytics to improve cardiovascular care: promise and challenges

By J. Rumsfeld, Karen E. Joynt, Thomas M. Maddox. Nature Reviews Cardiology, 2016

366 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Economics and Econometrics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/05425a0b1e8cd37c10250d520ae7cda14b611cf8

How researchers see this paper

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

In Cardiology and Cardiovascular Medicine

1. Alfonso Bueno-Orovio
2. Blanca Rodríguez
3. A. Ślęzak
4. Kornelia M. Batko
5. Anil Pandit
6. Nishita Mehta

In Economics and Econometrics

1. A. Ślęzak
2. Mark Potse
3. Kornelia M. Batko
4. Anil Pandit
5. Nishita Mehta

In Health Information Management

1. Zeeshan Ahmed
2. Khalid Mohamed
3. A. Ślęzak
4. Xinqi Dong
5. Kornelia M. Batko
6. Saman Zeeshan
7. Anil Pandit
8. Nishita Mehta

Citations in the corpus

• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “To effectively implement healthcare data analytic processes, various big data management challenges (1,9,13,14, 15,16,17,18,19) have to be overcome, which include inadequacy of analyzable clinical data (20); existence of multiple data standards, structures, types and formats; rapid growth in heterog”
• The ‘Digital Twin’ to enable the vision of precision cardiology (2020, positive): “14,15 Synergy between mechanistic and statistical models has shown value in aiding diagnosis, treatment, and prognosis evaluation.”
• Brief introduction of medical database and data mining technology in big data era (2020, background): “16,17 There are many values in big data applications and data collection in healthcare systems.”
• Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “The data may include administrative health data, biomarker data, biometric data (for example, from wearable technologies) and imaging, and may originate from many different sources, including EHRs, clinical registries, biobanks, the internet and patient self-reports 8 .”
• Concurrence of big data analytics and healthcare: A systematic review (2018, methodology): “Some of the primary sources of Big Data in healthcare are administrative databases (insurance claims and pharmaceuticals), clinical databases, electronic health record data [11], and laboratory information system data [39].”
• The use of Big Data Analytics in healthcare (2022, positive): “These regard the use of Big Data Analytics to diagnose specific conditions [47, 66, 69, 76], propose an approach that can be used in other healthcare applications and create mechanisms to identify “patients like me” [75, 80].”