5,032 research outputs found
Wind models and cross-site interpolation for the refugee reception islands in Greece
In this study, the wind data series from five locations in Aegean Sea
islands, the most active `hotspots' in terms of refugee influx during the
Oct/2015 - Jan/2016 period, are investigated. The analysis of the
three-per-site data series includes standard statistical analysis and
parametric distributions, auto-correlation analysis, cross-correlation analysis
between the sites, as well as various ARMA models for estimating the
feasibility and accuracy of such spatio-temporal linear regressors for
predictive analytics. Strong correlations are detected across specific sites
and appropriately trained ARMA(7,5) models achieve 1-day look-ahead error
(RMSE) of less than 1.9 km/h on average wind speed. The results show that such
data-driven statistical approaches are extremely useful in identifying
unexpected and sometimes counter-intuitive associations between the available
spatial data nodes, which is very important when designing corresponding models
for short-term forecasting of sea condition, especially average wave height and
direction, which is in fact what defines the associated weather risk of
crossing these passages in refugee influx patterns.Comment: 23 figures, 3 tables, 17 reference
Identification of refugee influx patterns in Greece via model-theoretic analysis of daily arrivals
The refugee crisis is perhaps the single most challenging problem for Europe
today. Hundreds of thousands of people have already traveled across dangerous
sea passages from Turkish shores to Greek islands, resulting in thousands of
dead and missing, despite the best rescue efforts from both sides. One of the
main reasons is the total lack of any early warning-alerting system, which
could provide some preparation time for the prompt and effective deployment of
resources at the hot zones. This work is such an attempt for a systemic
analysis of the refugee influx in Greece, aiming at (a) the statistical and
signal-level characterization of the smuggling networks and (b) the formulation
and preliminary assessment of such models for predictive purposes, i.e., as the
basis of such an early warning-alerting protocol. To our knowledge, this is the
first-ever attempt to design such a system, since this refugee crisis itself
and its geographical properties are unique (intense event handling, little or
no warning). The analysis employs a wide range of statistical, signal-based and
matrix factorization (decomposition) techniques, including linear &
linear-cosine regression, spectral analysis, ARMA, SVD, Probabilistic PCA, ICA,
K-SVD for Dictionary Learning, as well as fractal dimension analysis. It is
established that the behavioral patterns of the smuggling networks closely
match (as expected) the regular burst and pause periods of store-and-forward
networks in digital communications. There are also major periodic trends in the
range of 6.2-6.5 days and strong correlations in lags of four or more days,
with distinct preference in the Sunday-Monday 48-hour time frame. These results
show that such models can be used successfully for short-term forecasting of
the influx intensity, producing an invaluable operational asset for planners,
decision-makers and first-responders.Comment: 21 pages, 26 figures, 1 table, 23 equations, 72 reference
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
Machine learning is driving development across many fields in science and
engineering. A simple and efficient programming language could accelerate
applications of machine learning in various fields. Currently, the programming
languages most commonly used to develop machine learning algorithms include
Python, MATLAB, and C/C ++. However, none of these languages well balance both
efficiency and simplicity. The Julia language is a fast, easy-to-use, and
open-source programming language that was originally designed for
high-performance computing, which can well balance the efficiency and
simplicity. This paper summarizes the related research work and developments in
the application of the Julia language in machine learning. It first surveys the
popular machine learning algorithms that are developed in the Julia language.
Then, it investigates applications of the machine learning algorithms
implemented with the Julia language. Finally, it discusses the open issues and
the potential future directions that arise in the use of the Julia language in
machine learning.Comment: Published in Computer Science Revie
A predictive analytics approach to reducing avoidable hospital readmission
Hospital readmission has become a critical metric of quality and cost of
healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20%
of patients who are readmitted within 30 days of discharge. Although several
interventions such as transition care management and discharge reengineering
have been practiced in recent years, the effectiveness and sustainability
depends on how well they can identify and target patients at high risk of
rehospitalization. Based on the literature, most current risk prediction models
fail to reach an acceptable accuracy level; none of them considers patient's
history of readmission and impacts of patient attribute changes over time; and
they often do not discriminate between planned and unnecessary readmissions.
Tackling such drawbacks, we develop a new readmission metric based on
administrative data that can identify potentially avoidable readmissions from
all other types of readmission. We further propose a tree based classification
method to estimate the predicted probability of readmission that can directly
incorporate patient's history of readmission and risk factors changes over
time. The proposed methods are validated with 2011-12 Veterans Health
Administration data from inpatients hospitalized for heart failure, acute
myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in
the State of Michigan. Results shows improved discrimination power compared to
the literature (c-statistics>80%) and good calibration.Comment: 30 pages, 4 figures, 7 table
Simulation of Patient Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification
Introduction: An approach to building a hybrid simulation of patient flow is
introduced with a combination of data-driven methods for automation of model
identification. The approach is described with a conceptual framework and basic
methods for combination of different techniques. The implementation of the
proposed approach for simulation of acute coronary syndrome (ACS) was developed
and used within an experimental study. Methods: Combination of data, text, and
process mining techniques and machine learning approaches for analysis of
electronic health records (EHRs) with discrete-event simulation (DES) and
queueing theory for simulation of patient flow was proposed. The performed
analysis of EHRs for ACS patients enable identification of several classes of
clinical pathways (CPs) which were used to implement a more realistic
simulation of the patient flow. The developed solution was implemented using
Python libraries (SimPy, SciPy, and others). Results: The proposed approach
enables more realistic and detailed simulation of the patient flow within a
group of related departments. Experimental study shows that the improved
simulation of patient length of stay for ACS patient flow obtained from EHRs in
Federal Almazov North-west Medical Research Centre in Saint Petersburg, Russia.
Conclusion: The proposed approach, methods, and solutions provide a conceptual,
methodological, and programming framework for implementation of simulation of
complex and diverse scenarios within a flow of patients for different purposes:
decision making, training, management optimization, and others
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Deep learning has recently seen rapid development and received significant
attention due to its state-of-the-art performance on previously-thought hard
problems. However, because of the internal complexity and nonlinear structure
of deep neural networks, the underlying decision making processes for why these
models are achieving such performance are challenging and sometimes mystifying
to interpret. As deep learning spreads across domains, it is of paramount
importance that we equip users of deep learning with tools for understanding
when a model works correctly, when it fails, and ultimately how to improve its
performance. Standardized toolkits for building neural networks have helped
democratize deep learning; visual analytics systems have now been developed to
support model explanation, interpretation, debugging, and improvement. We
present a survey of the role of visual analytics in deep learning research,
which highlights its short yet impactful history and thoroughly summarizes the
state-of-the-art using a human-centered interrogative framework, focusing on
the Five W's and How (Why, Who, What, How, When, and Where). We conclude by
highlighting research directions and open research problems. This survey helps
researchers and practitioners in both visual analytics and deep learning to
quickly learn key aspects of this young and rapidly growing body of research,
whose impact spans a diverse range of domains.Comment: Under review for IEEE Transactions on Visualization and Computer
Graphics (TVCG
How 5G (and concomitant technologies) will revolutionize healthcare
In this paper, we build the case that 5G and concomitant emerging
technologies (such as IoT, big data, artificial intelligence, and machine
learning) will transform global healthcare systems in the near future. Our
optimism around 5G-enabled healthcare stems from a confluence of significant
technical pushes that are already at play: apart from the availability of
high-throughput low-latency wireless connectivity, other significant factors
include the democratization of computing through cloud computing; the
democratization of AI and cognitive computing (e.g., IBM Watson); and the
commoditization of data through crowdsourcing and digital exhaust. These
technologies together can finally crack a dysfunctional healthcare system that
has largely been impervious to technological innovations. We highlight the
persistent deficiencies of the current healthcare system, and then demonstrate
how the 5G-enabled healthcare revolution can fix these deficiencies. We also
highlight open technical research challenges, and potential pitfalls, that may
hinder the development of such a 5G-enabled health revolution
iNNvestigate neural networks!
In recent years, deep neural networks have revolutionized many application
domains of machine learning and are key components of many critical decision or
predictive processes. Therefore, it is crucial that domain specialists can
understand and analyze actions and pre- dictions, even of the most complex
neural network architectures. Despite these arguments neural networks are often
treated as black boxes. In the attempt to alleviate this short- coming many
analysis methods were proposed, yet the lack of reference implementations often
makes a systematic comparison between the methods a major effort. The presented
library iNNvestigate addresses this by providing a common interface and
out-of-the- box implementation for many analysis methods, including the
reference implementation for PatternNet and PatternAttribution as well as for
LRP-methods. To demonstrate the versatility of iNNvestigate, we provide an
analysis of image classifications for variety of state-of-the-art neural
network architectures
Surgical Data Science -- from Concepts to Clinical Translation
Recent developments in data science in general and machine learning in
particular have transformed the way experts envision the future of surgery.
Surgical data science is a new research field that aims to improve the quality
of interventional healthcare through the capture, organization, analysis and
modeling of data. While an increasing number of data-driven approaches and
clinical applications have been studied in the fields of radiological and
clinical data science, translational success stories are still lacking in
surgery. In this publication, we shed light on the underlying reasons and
provide a roadmap for future advances in the field. Based on an international
workshop involving leading researchers in the field of surgical data science,
we review current practice, key achievements and initiatives as well as
available standards and tools for a number of topics relevant to the field,
namely (1) technical infrastructure for data acquisition, storage and access in
the presence of regulatory constraints, (2) data annotation and sharing and (3)
data analytics. Drawing from this extensive review, we present current
challenges for technology development and (4) describe a roadmap for faster
clinical translation and exploitation of the full potential of surgical data
science
PyHealth: A Python Library for Health Predictive Models
Despite the explosion of interest in healthcare AI research, the
reproducibility and benchmarking of those research works are often limited due
to the lack of standard benchmark datasets and diverse evaluation metrics. To
address this reproducibility challenge, we develop PyHealth, an open-source
Python toolbox for developing various predictive models on healthcare data.
PyHealth consists of data preprocessing module, predictive modeling module,
and evaluation module. The target users of PyHealth are both computer science
researchers and healthcare data scientists. With PyHealth, they can conduct
complex machine learning pipelines on healthcare datasets with fewer than ten
lines of code. The data preprocessing module enables the transformation of
complex healthcare datasets such as longitudinal electronic health records,
medical images, continuous signals (e.g., electrocardiogram), and clinical
notes into machine learning friendly formats. The predictive modeling module
provides more than 30 machine learning models, including established ensemble
trees and deep neural network-based approaches, via a unified but extendable
API designed for both researchers and practitioners. The evaluation module
provides various evaluation strategies (e.g., cross-validation and
train-validation-test split) and predictive model metrics.
With robustness and scalability in mind, best practices such as unit testing,
continuous integration, code coverage, and interactive examples are introduced
in the library's development. PyHealth can be installed through the Python
Package Index (PyPI) or https://github.com/yzhao062/PyHealth
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