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.

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.

scholarly journals Data Driven Scene Classification to Analyze ADAS

2019 ◽  
Vol 8 (2S4) ◽  
pp. 117-119
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Autonomous Vehicles ◽  
Data Driven ◽  
Sensor Data ◽  
Data Logger ◽  
Huge Amount ◽  
Vehicle Data ◽  
Big Data Visualization ◽  
Analyze Data

In every sphere of life Big Data will be transformative. Data Visualization and Analytics plays an important role in decision making in various sectors. In autonomous vehicles data from various sensors and RADAR are stored in data logger, which is huge in size. To evaluate the performance of specific sensor manually is tedious task. This paper proposes an idea to create an interactive GUI framework to analyze the vehicle data and sensor data using big data visualization method. The framework contains various plots and plots are made interactive to analyze data in depth for all the scenarios of ADAS. It can be used to analyze the behavior of the vehicle at each instance of time interactively and time synchronized image frames are also incorporated with framework to see behavior of the plots. The paper proposes a Framework to analyze the huge amount vehicle data and sensor data which can be used to analyze the behavior of ADAS application.

A survey on big data-driven digital phenotyping of mental health

2019 ◽  
Vol 52 ◽  
pp. 290-307 ◽  
Author(s):  
Yunji Liang ◽  
Xiaolong Zheng ◽  
Daniel D. Zeng
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Data Driven ◽  
Digital Phenotyping

scholarly journals Implementing Big Data Analytics in Marketing Departments: Mixing Organic and Administered Approaches to Increase Data-Driven Decision Making

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 66
Author(s):  
Devon S. Johnson ◽  
Debika Sihi ◽  
Laurent Muzellec
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Survey Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Top Management ◽  
Data Driven Decision Making ◽  
Marketing Department

This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth interviews of marketing and analytics professionals in the US and Europe involved in the implementation of big data analytics (BDA) and was followed by a survey data of 298 marketing and analytics middle management professionals at United States based firms. The survey data supports the logic that BDA sensemaking is initiated by top management and is comprised of four primary activities: external knowledge acquisition, improving digitized data quality, big data analytics experimentation and big data analytics information dissemination. Top management drives progress toward data-driven decision-making by facilitating sensemaking and by increasing the influence of BDA skilled employees. This study suggests that while a shift toward enterprise analytics increases the quality of resource available to the marketing department, this approach could stymie the quality of marketing insights gained from BDA. This study presents a model of how to improve the quality of marketing insights and improve data-driven decision-making.

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

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