The inevitable application of big data to health care.

By T. Murdoch, A. Detsky. Journal of the American Medical Association (JAMA), 2013

1,624 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/53834f0ee8df731cf0e629cd594dce0afaaa3d97

How researchers see this paper

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

In Artificial Intelligence

In Health Information Management

Citations in the corpus

Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations (2018, background): “Yet, healthcare organizations continue to struggle to gain the benefits from their investments on big data analytics and some of them are skeptical about its power, although they invest in big data analytics in hope for healthcare transformation (Murdoch and Detsky, 2013; Shah and Pathak, 2014).”
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping (2017, background): “While structured data do not require complex processing prior to performing statistical tests and conducting machine learning tasks, the majority of recorded data exist in unstructured form.(11) Applying natural language processing (NLP) on the unstructured data in conjunction with analyzing the str”
A Bibliometric Analysis and Visualization of Medical Big Data Research (2018, background): “His paper entitled “ The Inevitable Application of Big Data to Health Care ” published in JAMA-Journal of the American Medical Association proposed that the use of patient and physician data collection may be an important way to improve the quality and efﬁciency of health care services [45].”
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction (2017, background): “Large-scale collections of electronic health records (EHRs) are becoming useful for understanding disease progress, early diagnosis, and personalized treatments for many clinical diseases (Murdoch and Detsky, 2013; Hripcsak and Albers, 2013; Ghassemi et al., 2015a).”
Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis (2019, background): “They are able to dig out useful information from a large amount of data which is difficult to be processed by doctors and thus, assist the medical decision making [16,35].”
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, background): “While structured data do not require complex processing prior to statistical tests and machine learning tasks, the majority of data exist in unstructured form [12].”
Challenges and Opportunities of Big Data in Health Care: A Systematic Review (2016, background): “Health care needs to follow their lead so that decisions regarding organizational objectives and goals can be met [4,11,12].”
A Neural Network Based Model for Predicting Psychological Conditions (2015, background): “With the recent increase in big data present in the healthcare domain [1,2], from medical organizations modernizing their operations through electronic health records (EHRs) and deploying new health information technology (HIT) systems, there exists an opportunity for applying predictive models on t”