Big Data Cohort Extraction to Facilitate Machine Learning to Improve Statin Treatment

2016 ◽  
Vol 39 (1) ◽  
pp. 42-62 ◽  
Author(s):  
Chih-Lin Chi ◽  
Jin Wang ◽  
Thomas R. Clancy ◽  
Jennifer G. Robinson ◽  
Peter J. Tonellato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Project Success ◽  
Statin Treatment ◽  
Data Driven ◽  
Domain Experts ◽  
Patient Cohort ◽  
Important Approach ◽  
Data Elements ◽  
Support Decision Making

Health care Big Data studies hold substantial promise for improving clinical practice. Among analytic tools, machine learning (ML) is an important approach that has been widely used by many industries for data-driven decision support. In Big Data, thousands of variables and millions of patient records are commonly encountered, but most data elements cannot be directly used to support decision making. Although many feature-selection tools can help identify relevant data, these tools are typically insufficient to determine a patient data cohort to support learning. Therefore, domain experts with nursing or clinic knowledge play critical roles in determining value criteria or the type of variables that should be included in the patient cohort to maximize project success. We demonstrate this process by extracting a patient cohort (37,506 individuals) to support our ML work (i.e., the production of a proactive strategy to prevent statin adverse events) from 130 million de-identified lives in the OptumLabs™ Data Warehouse.

Big data‐driven machine learning‐enabled traffic flow prediction

2018 ◽  
Vol 30 (9) ◽  
Author(s):  
Fanhui Kong ◽  
Jian Li ◽  
Bin Jiang ◽  
Tianyuan Zhang ◽  
Houbing Song
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Flow ◽  
Data Driven ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Big data need big theory too

2016 ◽  
Vol 374 (2080) ◽  
pp. 20160153 ◽  
Author(s):  
Peter V. Coveney ◽  
Edward R. Dougherty ◽  
Roger R. Highfield
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Conceptual Knowledge ◽  
Structural Characteristics ◽  
Multiscale Modelling ◽  
Neural Nets ◽  
Data Driven ◽  
Scientific Methods

The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

scholarly journals Connecting and linking neurocognitive, digital phenotyping, physiologic, psychophysical, neuroimaging, genomic, & sensor data with survey data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Charles E. Knott ◽  
Stephen Gomori ◽  
Mai Ngyuen ◽  
Susan Pedrazzani ◽  
Sridevi Sattaluri ◽  
...  
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Emergency Departments ◽  
Ecological Monitoring ◽  
Data Driven ◽  
Sensor Data ◽  
Trauma Patients ◽  
Long Term Effects ◽  
Digital Phenotyping ◽  
Data Elements

AbstractCombining survey data with alternative data sources (e.g., wearable technology, apps, physiological, ecological monitoring, genomic, neurocognitive assessments, brain imaging, and psychophysical data) to paint a complete biobehavioral picture of trauma patients comes with many complex system challenges and solutions. Starting in emergency departments and incorporating these diverse, broad, and separate data streams presents technical, operational, and logistical challenges but allows for a greater scientific understanding of the long-term effects of trauma. Our manuscript describes incorporating and prospectively linking these multi-dimensional big data elements into a clinical, observational study at US emergency departments with the goal to understand, prevent, and predict adverse posttraumatic neuropsychiatric sequelae (APNS) that affects over 40 million Americans annually. We outline key data-driven system challenges and solutions and investigate eligibility considerations, compliance, and response rate outcomes incorporating these diverse “big data” measures using integrated data-driven cross-discipline system architecture.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

2016 ◽  
Vol 23 (3) ◽  
pp. 269-278 ◽  
Author(s):  
R. Andrew Taylor ◽  
Joseph R. Pare ◽  
Arjun K. Venkatesh ◽  
Hani Mowafi ◽  
Edward R. Melnick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Big Data ◽  
Hospital Mortality ◽  
Data Driven ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Emergency Department Patients

scholarly journals Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

2014 ◽  
pp. 367-374 ◽  
Author(s):  
Pål Sundsøy ◽  
Johannes Bjelland ◽  
Asif M. Iqbal ◽  
Alex “Sandy” Pentland ◽  
Yves-Alexandre de Montjoye
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Driven
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