Issues in Mobile Health (Wendy Nilsen)
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The document discusses challenges with mHealth (mobile health) research. It notes the rapid explosion of data inputs and need for high throughput analyses to handle complex, streaming data from various sensors and sources. Key challenges include technology development timelines not aligning with the slow NIH funding process, the need for interdisciplinary teams over traditional academic models, and developing research methodologies for collecting and analyzing mHealth data. The document announces an upcoming workshop to assess alternate research methods to randomized controlled trials which are difficult in fast-paced technology, and NIH training institutes to improve skills around using mHealth in research and cross-disciplinary collaboration.
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Abstract
Effective patient adherence management strategies require better understanding of patient-generated data, including patient-reported data and measurements from devices and sensors, as key to assisting providers in learning more about their patients’needs and enhancing patient centric care. Gaining “meaningful use” of patient-generated data could ultimately lead to improvements in patient safety and outcomes. In this workshop, we review proof of concept studies using technology to assess patient health literacy and self-efficacy with the goal of providing timely intervention, remedy, and improvements in cost and quality of care. In particular, we focus on engagement-enabling technolgoies that can leverage non-clinical information sources and reflect patient activities in the “wild”. We look into barriers to adherence, patients and providers roles in improving adherence, and the use of technology to assist patients in staying on track. The speakers will address the issues related tothe integration of patient-generated data into everyday life and clinical practice and share lessons learned from implementing these designs in practice. This workshop aims to share requirements gathered for the design of next-generation healthcare systems, especially in areas where the explosive availability of patient-generated data is expected to make impacts.
Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
Waterloo Adv Bd Dec 00
- The document outlines initiatives by the Computer Science Department related to health informatics, including the establishment of a Center for Computer Science Research in Health Informatics and a graduate specialization in health informatics.
- It also describes the creation of an Education Program for Health Informatics Professionals to provide continuing education to IT and health professionals through distance learning courses in applied health informatics.
- A number of faculty members are identified who have research interests relevant to health informatics, and potential areas for future health informatics research are listed.
ORIP Strategic Plan 2016
ORIP supports research infrastructure through three main themes:
1) Developing models of human diseases by expanding access to animal models and developing new disease models.
2) Accelerating research discoveries by providing access to state-of-the-art instrumentation through shared instrumentation grants.
3) Training and diversifying the biomedical workforce by training veterinary scientists and supporting STEM education.
IBES Health and biomedical informatics
IBES Health and biomedical informaticsHealth and Biomedical Informatics Centre @ The University of Melbourne
Presentation given by Fernando Martin-Sanchez and Kathleen Gray at IBES (Institute for the Broadband-Enabled Society) Annual Symposium - Sept 2011Digital Access to the World's Literature: A Blueprint to Integrate Evidence w...
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Medicine and the Internet of Things
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What are scalable best practices to spread smart health?
Maximizing health and well-being requires quality decision-making and positive lifestyles across millions, if not billions, of individual decision-makers. How can we accelerate the adoption of smart health behaviors in scalable and systematic ways, ensuring benefits at both the individual and population levels, and empowering consumers, patients and professionals?
- Chair: Jayne Plunkett, Head of Casualty Reinsurance at Swiss Re, YGL Class of 2010
- Misha Pavel, Program Director of Smart and Connected Health at the National Science Foundation
- Dharma Singh Khalsa, President of the Alzheimer’s Research and Prevention Foundation
- Josh Wright, Managing Director of ideas42
This session took place at the 2013 SharpBrains Virtual Summit: http://sharpbrains.com/summit-2013/agenda/
의료의 미래, 디지털 헬스케어: 정신의학을 중심으로
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2) Previous research has found correlations between depressive symptom severity and certain location-based smartphone sensor data, such as increased location variance and disrupted circadian rhythms.
3) This study replicates previous findings using GPS smartphone sensor data collected from 48 college students over 10 weeks, finding significant correlations between depressive symptoms and location variance, entropy, and circadian movement patterns. The relationships were stronger when analyzing weekend versus weekday data.
Haladjian CV
This document is a curriculum vitae for Harry Haladjian, who is pursuing a Ph.D. in Cognitive Psychology from Rutgers University. It outlines his education, awards, teaching experience, research experience, skills, publications, and conference presentations. He has received several fellowships and awards for his research investigating visual attention and enumeration processes.
Obeid generic_2017-11
Jihad Obeid is an Associate Professor of Public Health Sciences at the Medical University of South Carolina (MUSC). He received his BS in Biology from the American University of Beirut and his MD from the same institution. He completed residency training in pediatrics at Duke University Medical Center and fellowships in pediatric endocrinology at Cornell University Medical College and medical informatics at Harvard-MIT. As Co-Director of the Biomedical Informatics Center at MUSC, he oversees several informatics initiatives and leads multiple projects funded by Clinical and Translational Science Awards. His research focuses on informed consent, research permissions management systems, and using electronic health records to support research recruitment. He has over 30
Researcher Dilemmas using Behavioral Big Data in Healthcare (INFORMS DMDA Wo...
Keynote by Galit Shmueli at 12th INFORMS Data Mining and Decision Analysis Workshop, Houston TX, Oct 21, 2017
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...
HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretabi...Pei-Yun Sabrina Hsueh
Chair/Moderator: Pei-Yun Sabrina HSUEH, PhD (IBM T.J. Watson Research Center)
Panelists: XinXin ZHU, Bian YANG, Ying-Kuen CHEUNG , Thomas WETTER, and Sanjoy DEY
a IBM T.J. Watson Research Center, USA
b Norwegian University of Science and Technology, Norway
c Mailman School of Public health, Columbia University, USA
d, Department of Biomedical Informatics, University of Washington, USA
e Department of Medical Informatics, University of Heidelberg, Germany
The rise of consumer health awareness and the recent advent of personal health management tools (including mobile and health wearable devices) have contributed to another shift transforming the healthcare landscape. Despite the rise of health consumers, the impact of user-generated health data remains to be validated. In fact, many applications are hinged on the interpretability issues of this sort of data. The aim of this panel is two-fold. First, this panel aims to review the key dimensions in the interpretability, spanning from quality and reliability to information security and trust management. Secondly, since similar issues and methodologies have been proposed in different application areas ranging from clinical decision support to behavioral interventions and clinical trials, the panelists will also discuss both the success stories and the areas that fall short. The opportunities and barriers identified can then serve as guidelines or action items individuals can bring to their organizations to further improve the interpretability of user-generated data. KathrynYoung_CV_032515
Kathryn E. Young has a Masters in Neuroscience from the University of Hartford and a BA in Psychology and Sociology from the University of Connecticut. She has over 3 years of experience as a clinical research assistant at Hartford Hospital, where she designs studies, analyzes neuroimaging and behavioral data, and coordinates multiple research protocols. Her areas of research focus include cognitive behavioral therapy for hoarding disorder, emotional regulation in adolescents, and reward processing in teens with ADHD.
PhRMA Some Early Thoughts
Presentation to PhRMA http://www.phrma.org/ on how the pharmaceutical industry might interface with the NIH around data.
Naf ppt
This document discusses evidence-based public health (EBPH). It defines EBPH as the development, implementation, and evaluation of effective programs and policies through applying scientific reasoning principles. The key steps of EBPH include quantifying the public health issue, conducting a literature review, developing and prioritizing program options, creating an action plan, and evaluating programs and policies. EBPH relies on diverse sources of evidence, both quantitative and qualitative research. It differs from evidence-based medicine in its focus on populations rather than individuals and emphasis on environmental and social factors.
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Bioinformatics introduction of biological database (NCBI), Introduction to bioinformatics.
major bioinformatics database.
important database of bioinformatics
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Annals of Behavioral Medicine Volume 49-Issue 1 Supplement-April 2015
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Annals of behavioral medicine volume 49 issue 1 supplement april 2015
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Medinfo2015 workshop-adherence mangement-patient_driven-publicized
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Kno.e.sis Approach to Impactful Research & Training for Exceptional Careers
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