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 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 Digital/computational phenotyping: What are the differences in the science and the ethics?

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110628
Author(s):  
Federica Lucivero ◽  
Nina Hallowell
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Digital Tools ◽  
Social Implications ◽  
Ethical Implications ◽  
Digital Phenotyping ◽  
The University ◽  
Compare And Contrast ◽  
Health Field ◽  
Digital Biomarkers

The concept of ‘digital phenotyping’ was originally developed by researchers in the mental health field, but it has travelled to other disciplines and areas. This commentary draws upon our experiences of working in two scientific projects that are based at the University of Oxford’s Big Data Institute – The RADAR-AD project and The Minerva Initiative – which are developing algorithmic phenotyping technologies. We describe and analyse the concepts of digital biomarkers and computational phenotyping that underlie these projects, explain how they are linked to other research in digital phenotyping and compare and contrast some of their epistemological and ethical implications. In particular, we argue that the phenotyping paradigm in both projects is grounded on an assumption of ‘objectivity’ that is articulated in different ways depending on the role that is given to the computational/digital tools. Using the concept of ‘affordance’, we show how specific functionalities relate to potential uses and social implications of these technologies and argue that it is important to distinguish among them as the concept of digital phenotyping is increasingly being used with a variety of meanings.

scholarly journals Precision agriculture and geospatial techniques for sustainable disease control

2021 ◽  
Author(s):  
Daniel P. Roberts ◽  
Nicholas M. Short ◽  
James Sill ◽  
Dilip K. Lakshman ◽  
Xiaojia Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Disease Control ◽  
Crop Production ◽  
Plant Disease ◽  
Production Systems ◽  
Cropping Systems ◽  
Data Driven ◽  
Agricultural Community ◽  
Plant Disease Control ◽  
Crop Germplasm

AbstractThe agricultural community is confronted with dual challenges; increasing production of nutritionally dense food and decreasing the impacts of these crop production systems on the land, water, and climate. Control of plant pathogens will figure prominently in meeting these challenges as plant diseases cause significant yield and economic losses to crops responsible for feeding a large portion of the world population. New approaches and technologies to enhance sustainability of crop production systems and, importantly, plant disease control need to be developed and adopted. By leveraging advanced geoinformatic techniques, advances in computing and sensing infrastructure (e.g., cloud-based, big data-driven applications) will aid in the monitoring and management of pesticides and biologicals, such as cover crops and beneficial microbes, to reduce the impact of plant disease control and cropping systems on the environment. This includes geospatial tools being developed to aid the farmer in managing cropping system and disease management strategies that are more sustainable but increasingly complex. Geoinformatics and cloud-based, big data-driven applications are also being enlisted to speed up crop germplasm improvement; crop germplasm that has enhanced tolerance to pathogens and abiotic stress and is in tune with different cropping systems and environmental conditions is needed. Finally, advanced geoinformatic techniques and advances in computing infrastructure allow a more collaborative framework amongst scientists, policymakers, and the agricultural community to speed the development, transfer, and adoption of these sustainable technologies.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

Sign in / Sign up

Export Citation Format

Share Document