IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

Hoyt, Robert; Snider, Dallas; Thompson, Carla J.; Mantravadi, S.

doi:10.2196/publichealth.5810

Cited by 51 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Build trust to avoid historical concerns and current fears of privacy loss and "big brother surveillance" through sustainable long-term community relationships (Challenge I) 5. Invest in data collection on area relevant small sample populations to address incompleteness of big data (Challenge II) 6. Encourage data sharing to benefit under-resourced minority-serving institutions and underrepresented minority researchers by research intensive institutions (Challenge III) ties Act (Public Law 96-516), the gap in educational attainment separating underrepresented minorities from Whites and Asians remains wide in mathematics, statistics, and computer sciences.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

172

138

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…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

172

138

View full text Add to dashboard Cite

show abstract

“…Watson for Oncology, (developed by International Business Machines Corporation in collaboration with Sloan Kettering Cancer Center), and the Microsoft Hanover Project (developed with Oregon Health and Science University's Knight Cancer Institute) both use deep learning to guide oncology treatment. [ 17–21 ] Google DeepMind has likewise partnered with UK National Health Service to predict and analyze risk factors for chronic disease. [ 22 ] These investments further support a strong developing trend of system‐wide application of AI in healthcare outcomes.…”

Section: Trends In Artificial Intelligence and Deep Learningmentioning

confidence: 99%

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

Yee

Drum

2020

Advanced Therapeutics

View full text Add to dashboard Cite

Clinical care increasingly relies on individualized, highly multidimensional data for patient phenotyping. This review focusses on two technologies which are contributing to this trend and, in their mutual overlap, may provide a novel and powerful contribution to future clinical care. Artificial intelligence (AI), in particular the technique of deep learning, is steadily making inroads to clinical care and has demonstrated the ability to extract clinically relevant features from complex datasets. Likewise, mass spectrometry (MS), in particular high-resolution MS, is gaining the ability to provide highly multidimensional datasets from patient samples with the quality necessary for clinical decision making. The convergence of these trends has resulted in the nascent application of deep learning analytics to high resolution mass spectra. Due to the large volumes of content provided by high resolution mass spectra and the ability of multilayer neural networks to extract subtle features from complex datasets, future integration of their mutual application is identified as a research area of high priority for clinical care. Factors are identified that have contributed to historical progress for each technology and recommendations are provided to accelerate the mutual application of deep learning and mass spectrometry techniques to patient care.

show abstract

“…The population statistics will enable identification of high-risk patient for early interventions. In addition, EMR of patients can also be used to predict patient outcomes by determining the best treatment decision [3] The utilization of advanced machine learning and artificial intelligence (AI), ________________________________________________ such as the IBM Watson [5] , has demonstrated significant benefits in the healthcare delivery system. Predictive modelling using real-time data was shown to reduce adverse drug reactions, improve disasters management in hospitals and reduce re-admissions for patients with specific disease.…”

Section: Introductionmentioning

confidence: 99%

Advancing Pharmacy Service using Big Data – Are We Fully Utilising the Big Data’s Potential Yet?

et al. 2018

View full text Add to dashboard Cite

Big data is anticipated to have large implications in clinical pharmacy, in view of its potential in enhancing precision medicine and to avoid medication error. However, it is equally debatable since such a powerful tool may also disrupt the need of pharmacist in healthcare industry. In this article, we commented the contribution of Big Data in various aspects of clinical pharmacy including advancing pharmaceutical care service, optimising drug supplies, managing clinical trials, and strengthening pharmacovigilance. The future direction of the usage of Big Data related to clinical pharmacy will be discussed. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

show abstract

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

Cited by 51 publications

References 7 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

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

Advancing Pharmacy Service using Big Data – Are We Fully Utilising the Big Data’s Potential Yet?

Contact Info

Product

Resources

About