–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Wu, Po-Yen; Cheng, C. C.; Kaddi, Chanchala; Venugopalan, Janani; Hoffman, Ryan; Wang, May D.

doi:10.1109/tbme.2016.2573285

Cited by 230 publications

(80 citation statements)

References 162 publications

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“…A high-genomic content in Electronic Health Records (EHRs) could be very useful to uncovering existing knowledge discrepancies in diabetes data analyses (Capobianco, 2017). Some current applications of Big Data analytics in precision medicine include (Wu et al, 2017):…”

Section: Methodsmentioning

confidence: 99%

Healthcare Driven by Big Data Analytics

Alexander

Wang²

2018

American Journal of Engineering and Applied Sciences

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Text messages and social network posts are often included in big data and are frequently invaluable sources of health data. When machine learning or data mining is used, it is important to perform an automatic process for integrating all available and related health data. Artificial Neural Network (ANN) is one of the machine learning methods flexible in using algorithms to detect complicated nonlinear relationships within huge datasets. Pharmacokinetics uses genetics to individualize drug therapy. Because genetic and pharmacological analysis often require large scale computation, Big Data analytics has shown potential in this area. Personal data from various sensors can often be used for making health and treatment recommendations, taking appropriate action for a patient's lifestyle choices and early diagnosis vital to advancing quality of care. Big data techniques such as hadoop and spark have been used in various areas of healthcare and big data analytics has been employed in great datasets to expose hidden patterns or correlations for effective decision-making. Challenges of big data in healthcare, concerned with gathering material from multifaceted heterogonous patient sources still exist, although hadoop has been employed as the processing unit for heterogeneous data gathered from various body sensors. The most critical necessity in a healthcare big data system is the data security; however, users are continuously applying big data-driven strategies to solve the various problems and challenges.

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Section: Methodsmentioning

confidence: 99%

Healthcare Driven by Big Data Analytics

Alexander

Wang²

2018

American Journal of Engineering and Applied Sciences

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“…In the next step of the scientific process both hypothesis- and data-driven scientists (in experimental or quasi-experimental studies) test, their specific hypotheses, either by experimentation (hypothesis-driven science) or by computationally modeling the data (data-driven science) (Figure 7 D). Data-intensive models are concerned with relating an outcome of interest ( y ) with a large number of input or predictor variables ( x ), to determine the nature of the dependence between the outcome and the predictor(s) (i.e., y in relation to x ) ( 118 ); for example, modeling the relationship between selected clinical signs or molecular measures from patients (the predictors, x ) with likelihood of survival or response to a particular treatment (the outcome, y ) ( 120 ). Data-driven modeling includes either inferential models, which use the use the full data set to infer meaning about the cohort being modeled, or predictive models, which attempt to predict outcomes for individuals rather than providing summaries of the population (or data cohort) ( 121 ).…”

Section: Data-driven Science: a Paradigm Shiftmentioning

confidence: 99%

“…Typically, modeling efforts focus on classification or regression problems. Regression problems typically involve on estimating the strength and directionality of the relationship between x and y , whereas classification involves building models that can assign a new observation x (typically a patient) to a known class (e.g., likely has the diagnosis, likely to respond to treatment) ( 120 ).…”

Section: Data-driven Science: a Paradigm Shiftmentioning

confidence: 99%

The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

McCue

McCoy

2017

Front. Vet. Sci.

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Advances in high-throughput molecular biology and electronic health records (EHR), coupled with increasing computer capabilities have resulted in an increased interest in the use of big data in health care. Big data require collection and analysis of data at an unprecedented scale and represents a paradigm shift in health care, offering (1) the capacity to generate new knowledge more quickly than traditional scientific approaches; (2) unbiased collection and analysis of data; and (3) a holistic understanding of biology and pathophysiology. Big data promises more personalized and precision medicine for patients with improved accuracy and earlier diagnosis, and therapy tailored to an individual’s unique combination of genes, environmental risk, and precise disease phenotype. This promise comes from data collected from numerous sources, ranging from molecules to cells, to tissues, to individuals and populations—and the integration of these data into networks that improve understanding of heath and disease. Big data-driven science should play a role in propelling comparative medicine and “one medicine” (i.e., the shared physiology, pathophysiology, and disease risk factors across species) forward. Merging of data from EHR across institutions will give access to patient data on a scale previously unimaginable, allowing for precise phenotype definition and objective evaluation of risk factors and response to therapy. High-throughput molecular data will give insight into previously unexplored molecular pathophysiology and disease etiology. Investigation and integration of big data from a variety of sources will result in stronger parallels drawn at the molecular level between human and animal disease, allow for predictive modeling of infectious disease and identification of key areas of intervention, and facilitate step-changes in our understanding of disease that can make a substantial impact on animal and human health. However, the use of big data comes with significant challenges. Here we explore the scope of “big data,” including its opportunities, its limitations, and what is needed capitalize on big data in one medicine.

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“…The advent of platforms that can measure panels of hundreds or thousands of biomarkers presents new opportunities for developing diagnostic tests not only to detect disease, but to stratify people by risk and to predict response to therapy. It is widely expected that this will lead to a new era of "precision medicine" (1).…”

Section: Introductionmentioning

confidence: 99%

Quantifying performance of a diagnostic test as the expected information for discrimination: Relation to the C-statistic

McKeigue

2018

Stat Methods Med Res

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Although the C-statistic is widely used for evaluating the performance of diagnostic tests, its limitations for evaluating the predictive performance of biomarker panels have been widely discussed. The increment in C obtained by adding a new biomarker to a predictive model has no direct interpretation, and the relevance of the C-statistic to risk stratification is not obvious. This paper proposes that the C-statistic should be replaced by the expected information for discriminating between cases and non-cases (expected weight of evidence, denoted as Λ), and that the strength of evidence favouring one model over another should be evaluated by cross-validation as the difference in test log-likelihoods. Contributions of independent variables to predictive performance are additive on the scale of Λ. Where the effective number of independent predictors is large, the value of Λ is sufficient to characterize fully how the predictor will stratify risk in a population with given prior probability of disease, and the C-statistic can be interpreted as a mapping of Λ to the interval from 0.5 to 1. Even where this asymptotic relationship does not hold, there is a one-to-one mapping between the distributions in cases and non-cases of the weight of evidence favouring case over non-case status, and the quantiles of these distributions can be used to calculate how the predictor will stratify risk. This proposed approach to reporting predictive performance is demonstrated by analysis of a dataset on the contribution of microbiome profile to diagnosis of colorectal cancer.

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–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Cited by 230 publications

References 162 publications

Healthcare Driven by Big Data Analytics

Healthcare Driven by Big Data Analytics

The Scope of Big Data in One Medicine: Unprecedented Opportunities and Challenges

Quantifying performance of a diagnostic test as the expected information for discrimination: Relation to the C-statistic

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