IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Chen, Ying; Argentinis, JD Elenee; Weber, Griff

doi:10.1016/j.clinthera.2015.12.001

Cited by 360 publications

(221 citation statements)

References 26 publications

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“…The growth of publicly traded companies in this arena suggests a belief in future profits in digital health care. 6,7 The question we address is, will the introduction of Big Data into clinical practice and health care research contribute to increasing health disparities or to decreasing them?…”

Section: Introductionmentioning

confidence: 99%

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Zhang

Pérez‐Stable²,

Bourne³

et al. 2017

Ethn Dis

164

138

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Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.Ethn.Dis;2017;27(2):95-106; doi:10.18865/ed.27.2.95.

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Section: Introductionmentioning

confidence: 99%

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Zhang

Pérez‐Stable²,

Bourne³

et al. 2017

Ethn Dis

164

138

View full text Add to dashboard Cite

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“…Although modern chemical processing is mainly automatic, abnormal situations still rely on human operators to intervene. Abnormal situations are costly and can lead to injury and loss of lives [8].…”

Section: Challengementioning

confidence: 99%

Big Data in Chemical Industry

2017

IJASRE

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The overwhelming amount of computerized information that organizations and businesses generate and the process is growing so large that the term "big data" is commonly used to describe the situation. Big data is being used by chemical companies to enhance manufacturing, fine-tune pricing, improve marketing, and support innovation. It is creating great market opportunities in the chemical industry. This paper presents a short essay on how big data is currently being used in the chemical industry.

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“…An example of cognitive computing is the system developed by IBM named Watson (IBM Watson Health Imaging, Armonk, NY, USA). It strives to organize available information and present it in a contextually relevant, probability-driven manner to assist healthcare professionals in an objective manner, whether at a reading workstation or at the point-of-care [51]. An important change is underway.…”

Section: Introductionmentioning

confidence: 99%

To share or not to share? Expected pros and cons of data sharing in radiological research

et al. 2018

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The aims of this paper are to illustrate the trend towards data sharing, i.e. the regulated availability of the original patientlevel data obtained during a study, and to discuss the expected advantages (pros) and disadvantages (cons) of data sharing in radiological research. Expected pros include the potential for verification of original results with alternative or supplementary analyses (including estimation of reproducibility), advancement of knowledge by providing new results by testing new hypotheses (not explored by the original authors) on preexisting databases, larger scale analyses based on individualpatient data, enhanced multidisciplinary cooperation, reduced publication of false studies, improved clinical practice, and reduced cost and time for clinical research. Expected cons are outlined as the risk that the original authors could not exploit the entire potential of the data they obtained, possible failures in patients' privacy protection, technical barriers such as the lack of standard formats, and possible data misinterpretation. Finally, open issues regarding data ownership, the role of individual patients, advocacy groups and funding institutions in decision making about sharing of data and images are discussed. Key Points• Regulated availability of patient-level data of published clinical studies (data-sharing) is expected.• Expected benefits include verification/advancement of knowledge, reduced cost/time of research, clinical improvement.• Potential drawbacks include faults in patients' identity protection and data misinterpretation.

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IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research

Cited by 360 publications

References 26 publications

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century

Big Data in Chemical Industry

To share or not to share? Expected pros and cons of data sharing in radiological research

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