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Literature DB >> 28263935

The Promise of Big Data: Opportunities and Challenges.

Harlan M Krumholz¹.

Abstract

Entities: Chemical Disease Gene Species

Mesh：
Statistics as Topic

Year: 2016 PMID： 28263935 PMCID： PMC5459377 DOI： 10.1161/CIRCOUTCOMES.116.003366

Source DB: PubMed Journal: Circ Cardiovasc Qual Outcomes ISSN： 1941-7713

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10 in total

1. Association Between Physician Teamwork and Health System Outcomes After Coronary Artery Bypass Grafting.

Authors: John M Hollingsworth; Russell J Funk; Spencer A Garrison; Jason Owen-Smith; Samuel A Kaufman; Francis D Pagani; Brahmajee K Nallamothu
Journal: Circ Cardiovasc Qual Outcomes Date: 2016-11-08

2. Analysis of Machine Learning Techniques for Heart Failure Readmissions.

Authors: Bobak J Mortazavi; Nicholas S Downing; Emily M Bucholz; Kumar Dharmarajan; Ajay Manhapra; Shu-Xia Li; Sahand N Negahban; Harlan M Krumholz
Journal: Circ Cardiovasc Qual Outcomes Date: 2016-11-08

3. Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record.

Authors: Rahul C Deo; Brahmajee K Nallamothu
Journal: Circ Cardiovasc Qual Outcomes Date: 2016-11-08

4. Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

Authors: Kenney Ng; Steven R Steinhubl; Christopher deFilippi; Sanjoy Dey; Walter F Stewart
Journal: Circ Cardiovasc Qual Outcomes Date: 2016-11-08

1 in total

1. Effective Patient Care in a Data-Driven World.

Authors: Andrew Grace
Journal: Arrhythm Electrophysiol Rev Date: 2017-08

1 in total

The Promise of Big Data: Opportunities and Challenges.

1. Association Between Physician Teamwork and Health System Outcomes After Coronary Artery Bypass Grafting.

2. Analysis of Machine Learning Techniques for Heart Failure Readmissions.

3. Learning About Machine Learning: The Promise and Pitfalls of Big Data and the Electronic Health Record.

4. Early Detection of Heart Failure Using Electronic Health Records: Practical Implications for Time Before Diagnosis, Data Diversity, Data Quantity, and Data Density.

5. Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

6. Identifying Drug-Drug Interactions by Data Mining: A Pilot Study of Warfarin-Associated Drug Interactions.

7. Data Science in Healthcare: Implications for Early Career Investigators.

8. Big Science: Research Collaboration for Evidence-Based Care.

9. Can Big Data Fulfill Its Promise?

10. Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach.

1. Effective Patient Care in a Data-Driven World.