10,214 research outputs found
Cross-Modal Health State Estimation
Individuals create and consume more diverse data about themselves today than
any time in history. Sources of this data include wearable devices, images,
social media, geospatial information and more. A tremendous opportunity rests
within cross-modal data analysis that leverages existing domain knowledge
methods to understand and guide human health. Especially in chronic diseases,
current medical practice uses a combination of sparse hospital based biological
metrics (blood tests, expensive imaging, etc.) to understand the evolving
health status of an individual. Future health systems must integrate data
created at the individual level to better understand health status perpetually,
especially in a cybernetic framework. In this work we fuse multiple user
created and open source data streams along with established biomedical domain
knowledge to give two types of quantitative state estimates of cardiovascular
health. First, we use wearable devices to calculate cardiorespiratory fitness
(CRF), a known quantitative leading predictor of heart disease which is not
routinely collected in clinical settings. Second, we estimate inherent genetic
traits, living environmental risks, circadian rhythm, and biological metrics
from a diverse dataset. Our experimental results on 24 subjects demonstrate how
multi-modal data can provide personalized health insight. Understanding the
dynamic nature of health status will pave the way for better health based
recommendation engines, better clinical decision making and positive lifestyle
changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul,
Korea, ACM ISBN 978-1-4503-5665-7/18/1
Network-based stratification of tumor mutations.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence
Temporal Fidelity in Dynamic Social Networks
It has recently become possible to record detailed social interactions in
large social systems with high resolution. As we study these datasets, human
social interactions display patterns that emerge at multiple time scales, from
minutes to months. On a fundamental level, understanding of the network
dynamics can be used to inform the process of measuring social networks. The
details of measurement are of particular importance when considering dynamic
processes where minute-to-minute details are important, because collection of
physical proximity interactions with high temporal resolution is difficult and
expensive. Here, we consider the dynamic network of proximity-interactions
between approximately 500 individuals participating in the Copenhagen Networks
Study. We show that in order to accurately model spreading processes in the
network, the dynamic processes that occur on the order of minutes are essential
and must be included in the analysis
Testing the Integrated Theory of Health Behaviour Change for Postpartum Weight Management
Aim.â This is a report of a correlational study to test the Integrated Theory of Health Behaviour Change within the context of postpartum weight selfâmanagement including the impact of race/ethnicity and weight classification.
Background.â Women experiencing childbirth face increasing challenges to manage their weight postpartum. Little is known about womenâs weight selfâmanagement during the complex physiological and psychosocial transition of the postpartum period.
Methods.â Data were collected during the birth hospitalization and 4âmonths postbirth during 2005 and 2006. A quota sample of 250 postpartum women using two strata, race/ethnicity and prepregnant weight classification, were enrolled; 179 women completed the followâup survey. A survey questionnaire measured concepts from the Integrated Theory of Health Behaviour Change concepts, including knowledge and beliefs (selfâefficacy, outcome expectancy and goal congruence), selfâregulation skills and abilities, and social facilitation (social support and social influence) and the proximal outcome of weight retention. Factor analysis identified 5 factors consistent with the theoretical concepts that accounted for 47¡1% of total survey variance.
Results.â Model testing using path analysis explored the relationship among factors. The final model explained 25¡7% of the variance in self regulation at 4âmonths, but did not explain weight retention. The contribution of select concepts to total variance was different for Caucasian and African American women, but not by weight classification.
Conclusions.â Findings support use of theoretical concepts and relationships to understand postpartum weight selfâmanagement. The different relationships among concepts in Caucasian and African American women should be considered in planning targeted postpartum weight selfâmanagement interventions
Recommended from our members
Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile (personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.National Institute of Health (NIH)/Office of the Director Precision Medicine Initiative [1UG3OD023171-01]; Precision Medicine Initiative of the Center for Biomedical Informatics and Biostatistics of the University of Arizona Health Sciences; NIH/National Heart, Lung, and Blood Institute [HL126609-01, HL132523, U01 HL125208]; NIH/National Cancer Institute [P30CA023074, 1R01CA190696-01]; NIH/National Institute of Allergy and Infectious Diseases [U01AI122275-01]Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
- âŚ