4,089 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
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The evolution of public health genomics: Exploring its past, present, and future
Public health genomics has evolved to responsibly integrate advancements in genomics into the fields of personalized medicine and public health. Appropriate, effective and sustainable integration of genomics into healthcare requires an organized approach. This paper outlines the history that led to the emergence of public health genomics as a distinguishable field. In addition, a range of activities are described that illustrate how genomics can be incorporated into public health practice. Finally, it presents the evolution of public health genomics into the new era of âprecision public health.
The role of artificial intelligence in healthcare: a structured literature review
BACKGROUND/INTRODUCTION: Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. METHODS: The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. RESULTS: The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. CONCLUSIONS: The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field
General report & recommendations in predictive, preventive and personalised medicine 2012: white paper of the European Association for Predictive, Preventive and Personalised Medicine
This report is the collective product of word-leading experts working in the branches of integrative medicine by predictive, preventive and personalised medicine (PPPM) under the coordination of the European Association for Predictive, Preventive and Personalised Medicine. The general report has been prepared as the consortium document proposed at the EPMA World Congress 2011 which took place in Bonn, Germany. This forum analyzed the overall deficits and trends relevant for the top-science and daily practice in PPPM focused on the patient. Follow-up consultations resulted in a package of recommendations for consideration by research units, educators, healthcare industry, policy-makers, and funding bodies to cover the current knowledge deficit in the field and to introduce integrative approaches for advanced diagnostics, targeted prevention, treatments tailored to the person and cost-effective healthcare
Systems medicine and integrated care to combat chronic noncommunicable diseases
We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems
Teaching deep learning causal effects improves predictive performance
Causal inference is a powerful statistical methodology for explanatory
analysis and individualized treatment effect (ITE) estimation, a prominent
causal inference task that has become a fundamental research problem. ITE
estimation, when performed naively, tends to produce biased estimates. To
obtain unbiased estimates, counterfactual information is needed, which is not
directly observable from data. Based on mature domain knowledge, reliable
traditional methods to estimate ITE exist. In recent years, neural networks
have been widely used in clinical studies. Specifically, recurrent neural
networks (RNN) have been applied to temporal Electronic Health Records (EHR)
data analysis. However, RNNs are not guaranteed to automatically discover
causal knowledge, correctly estimate counterfactual information, and thus
correctly estimate the ITE. This lack of correct ITE estimates can hinder the
performance of the model. In this work we study whether RNNs can be guided to
correctly incorporate ITE-related knowledge and whether this improves
predictive performance. Specifically, we first describe a Causal-Temporal
Structure for temporal EHR data; then based on this structure, we estimate
sequential ITE along the timeline, using sequential Propensity Score Matching
(PSM); and finally, we propose a knowledge-guided neural network methodology to
incorporate estimated ITE. We demonstrate on real-world and synthetic data
(where the actual ITEs are known) that the proposed methodology can
significantly improve the prediction performance of RNN.Comment: 9 pages, 8 figures, in the process of SDM 202
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