3,665 research outputs found
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
A Backend Framework for the Efficient Management of Power System Measurements
Increased adoption and deployment of phasor measurement units (PMU) has
provided valuable fine-grained data over the grid. Analysis over these data can
provide insight into the health of the grid, thereby improving control over
operations. Realizing this data-driven control, however, requires validating,
processing and storing massive amounts of PMU data. This paper describes a PMU
data management system that supports input from multiple PMU data streams,
features an event-detection algorithm, and provides an efficient method for
retrieving archival data. The event-detection algorithm rapidly correlates
multiple PMU data streams, providing details on events occurring within the
power system. The event-detection algorithm feeds into a visualization
component, allowing operators to recognize events as they occur. The indexing
and data retrieval mechanism facilitates fast access to archived PMU data.
Using this method, we achieved over 30x speedup for queries with high
selectivity. With the development of these two components, we have developed a
system that allows efficient analysis of multiple time-aligned PMU data
streams.Comment: Published in Electric Power Systems Research (2016), not available
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Two Procedures for Robust Monitoring of Probability Distributions of Economic Data Streams induced by Depth Functions
Data streams (streaming data) consist of transiently observed, evolving in
time, multidimensional data sequences that challenge our computational and/or
inferential capabilities. In this paper we propose user friendly approaches for
robust monitoring of selected properties of unconditional and conditional
distribution of the stream basing on depth functions. Our proposals are robust
to a small fraction of outliers and/or inliers but sensitive to a regime change
of the stream at the same time. Their implementations are available in our free
R package DepthProc.Comment: Operations Research and Decisions, vol. 25, No. 1, 201
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