Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

By David W. Bates, Suchi Saria, Lucila Ohno-Machado, Anand Shah, G. Escobar. Health Affairs, 2014

1,105 citations recorded by the source corpus.

Subfields: General Health Professions, Health Information Management

View Paper: https://www.semanticscholar.org/paper/5b023b7169217d965be03b6cdf196142bd507806

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Citations in the corpus

Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, background): “The pervasiveness of computational systems in healthcare environments led to the widespread availability of electronic health data [1].”
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches (2017, background): “The Role of Radiomics in Big Data Radiomic studies follow the theme of emerging big data in health care that concerns mining vast quantities of biomedical data.(75,76) A typical concern of radiomics in big data relates to the management of growing image data, gene expression profiles, and the associ”
Disease Prediction by Machine Learning Over Big Data From Healthcare Communities (2017, background): “[10] proposed six applications of big data in the field of healthcare.”
Why Social Relationships Are Important for Physical Health: A Systems Approach to Understanding and Modifying Risk and Protection (2018, background): “Whereas previous assessment attempts, particularly those that are population based, were designed with brevity in mind, we now have the capability to use big data to identify those who may be at greatest risk (Bates et al. 2014).”
State of the art review: the data revolution in critical care (2015, background): “Some of the biggest opportunities for Big Data to make practical gains quickly are focused on the most expensive parts of current clinical practice: Reliable, predictive alerting and retrospective reporting analytics for high-cost patients, readmissions, triage, clinical decompensation, adverse even”
Multitask learning and benchmarking with clinical time series data (2017, background): “Such problems are not new,1 but the increased availability of digital clinical data and recent high-pro le machine learning successes [20, 57] have sparked a renewed interest in them.”
An integrated big data analytics-enabled transformation model: Application to health care (2018, methodology): “They use the traced data to implement drug – drug and drug – allergy interactions checks for EHR reporting and thus are able to identify drug-related risks at an earlier stage [1].”
Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. (2016, background): “2,10,11,14 As big data analytic methods are introduced into clinical practice, future efforts should seek to move from generalizable rules to generalizable methods that utilize the richness of local data.”
Scalable and accurate deep learning with electronic health records (2018, background): “It is widely suspected that use such efforts, if successful, could provide major benefits not only for patient safety and quality but also in reducing health care costs [4, 33, 26, 42].”
Comparison of Variable Selection Methods for Clinical Predictive Modeling (2018, methodology): “Modern data modeling methods, mostly derived from the machine learning literature, are also becoming widely available and are starting to be used by clinical researchers [2–4].”
Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors (2015, positive): “Successful examples of previously deployed large-scale risk assessment models include hospital readmission models,(4,5) disease onset prediction, and prediction of healthcare utilization and cost.(14) Type 2 diabetes is a global public health challenge.”
Concurrence of big data analytics and healthcare: A systematic review (2018, background): “personalization of predictions, targeted-treatment and cost-effectiveness of care, and reduction in waste of resources; and by giving actionable recommendations to individuals it encourages them maintain themselves in good health [10,18,26].”
From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare (2015, background): “Minin g local information included in EHR data-aware houses has a lready proved an effective way of managing a wide range of healthcare challenges such as supporting disease ma nagement system [66, 67], pharmacovigilance [68], building m odels for predicting health risk assessment [69, 70], communi ”
A targeted real-time early warning score (TREWScore) for septic shock (2015, background): “The adoption of the Affordable Care Act has added to the growing excitement around predictive models derived from electronic health data in a variety of applications (24), including discharge planning (25), risk stratification (26, 27), and identification of acute adverse events (28, 29).”