3,134 research outputs found
Critical Transitions in Intensive Care Units: A Sepsis Case Study
The progression of complex human diseases is associated with critical
transitions across dynamical regimes. These transitions often spawn
early-warning signals and provide insights into the underlying disease-driving
mechanisms. In this paper, we propose a computational method based on surprise
loss (SL) to discover data-driven indicators of such transitions in a
multivariate time series dataset of septic shock and non-sepsis patient cohorts
(MIMIC-III database). The core idea of SL is to train a mathematical model on
time series in an unsupervised fashion and to quantify the deterioration of the
model's forecast (out-of-sample) performance relative to its past (in-sample)
performance. Considering the highest value of the moving average of SL as a
critical transition, our retrospective analysis revealed that critical
transitions occurred at a median of over 35 hours before the onset of septic
shock, which suggests the applicability of our method as an early-warning
indicator. Furthermore, we show that clinical variables at critical-transition
regions are significantly different between septic shock and non-sepsis
cohorts. Therefore, our paper contributes a critical-transition-based
data-sampling strategy that can be utilized for further analysis, such as
patient classification. Moreover, our method outperformed other indicators of
critical transition in complex systems, such as temporal autocorrelation and
variance.Comment: 16 pages, 8 figures, 2 table
Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine
learning. Successful adoption of electronic health records (EHRs) created an
explosion in digital clinical data available for analysis, but progress in
machine learning for healthcare research has been difficult to measure because
of the absence of publicly available benchmark data sets. To address this
problem, we propose four clinical prediction benchmarks using data derived from
the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
database. These tasks cover a range of clinical problems including modeling
risk of mortality, forecasting length of stay, detecting physiologic decline,
and phenotype classification. We propose strong linear and neural baselines for
all four tasks and evaluate the effect of deep supervision, multitask training
and data-specific architectural modifications on the performance of neural
models.Comment: This version of the paper adds details about the generation of the
benchmark tasks and describes improved neural baseline
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient
information and measurements from a multitude of monitoring systems. The
limited ability of humans to process such complex information hinders
physicians to readily recognize and act on early signs of patient
deterioration. We used machine learning to develop an early warning system for
circulatory failure based on a high-resolution ICU database with 240 patient
years of data. This automatic system predicts 90.0% of circulatory failure
events (prevalence 3.1%), with 81.8% identified more than two hours in advance,
resulting in an area under the receiver operating characteristic curve of 94.0%
and area under the precision-recall curve of 63.0%. The model was externally
validated in a large independent patient cohort.Comment: 5 main figures, 1 main table, 13 supplementary figures, 5
supplementary tables; 250ppi image
SLEEPNET: Automated Sleep Staging System via Deep Learning
Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect
50-70 million adults in the United States (Hillman et al., 2006). Overnight
polysomnography (PSG), including brain monitoring using electroencephalography
(EEG), is a central component of the diagnostic evaluation for sleep disorders.
While PSG is conventionally performed by trained technologists, the recent rise
of powerful neural network learning algorithms combined with large
physiological datasets offers the possibility of automation, potentially making
expert-level sleep analysis more widely available. We propose SLEEPNET (Sleep
EEG neural network), a deployed annotation tool for sleep staging. SLEEPNET
uses a deep recurrent neural network trained on the largest sleep physiology
database assembled to date, consisting of PSGs from over 10,000 patients from
the Massachusetts General Hospital (MGH) Sleep Laboratory. SLEEPNET achieves
human-level annotation performance on an independent test set of 1,000 EEGs,
with an average accuracy of 85.76% and algorithm-expert inter-rater agreement
(IRA) of kappa = 79.46%, comparable to expert-expert IRA
Riemannian geometry applied to detection of respiratory states from EEG signals: the basis for a brain-ventilator interface
During mechanical ventilation, patient-ventilator disharmony is frequently
observed and may result in increased breathing effort, compromising the
patient's comfort and recovery. This circumstance requires clinical
intervention and becomes challenging when verbal communication is difficult. In
this work, we propose a brain computer interface (BCI) to automatically and
non-invasively detect patient-ventilator disharmony from
electroencephalographic (EEG) signals: a brain-ventilator interface (BVI). Our
framework exploits the cortical activation provoked by the inspiratory
compensation when the subject and the ventilator are desynchronized. Use of a
one-class approach and Riemannian geometry of EEG covariance matrices allows
effective classification of respiratory states. The BVI is validated on nine
healthy subjects that performed different respiratory tasks that mimic a
patient-ventilator disharmony. Classification performances, in terms of areas
under ROC curves, are significantly improved using EEG signals compared to
detection based on air flow. Reduction in the number of electrodes that can
achieve discrimination can often be desirable (e.g. for portable BCI systems).
By using an iterative channel selection technique, the Common Highest Order
Ranking (CHOrRa), we find that a reduced set of electrodes (n=6) can slightly
improve for an intra-subject configuration, and it still provides fairly good
performances for a general inter-subject setting. Results support the
discriminant capacity of our approach to identify anomalous respiratory states,
by learning from a training set containing only normal respiratory epochs. The
proposed framework opens the door to brain-ventilator interfaces for monitoring
patient's breathing comfort and adapting ventilator parameters to patient
respiratory needs.Comment: 14 pages, 7 figure
Discovering shared and individual latent structure in multiple time series
This paper proposes a nonparametric Bayesian method for exploratory data
analysis and feature construction in continuous time series. Our method focuses
on understanding shared features in a set of time series that exhibit
significant individual variability. Our method builds on the framework of
latent Diricihlet allocation (LDA) and its extension to hierarchical Dirichlet
processes, which allows us to characterize each series as switching between
latent ``topics'', where each topic is characterized as a distribution over
``words'' that specify the series dynamics. However, unlike standard
applications of LDA, we discover the words as we learn the model. We apply this
model to the task of tracking the physiological signals of premature infants;
our model obtains clinically significant insights as well as useful features
for supervised learning tasks.Comment: Additional supplementary section in tex fil
A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions
Whenever countries are threatened by a pandemic, as is the case with the
COVID-19 virus, governments should take the right actions to safeguard public
health as well as to mitigate the negative effects on the economy. In this
regard, there are two completely different approaches governments can take: a
restrictive one, in which drastic measures such as self-isolation can seriously
damage the economy, and a more liberal one, where more relaxed restrictions may
put at risk a high percentage of the population. The optimal approach could be
somewhere in between, and, in order to make the right decisions, it is
necessary to accurately estimate the future effects of taking one or other
measures. In this paper, we use the SEIR epidemiological model (Susceptible -
Exposed - Infected - Recovered) for infectious diseases to represent the
evolution of the virus COVID-19 over time in the population. To optimize the
best sequences of actions governments can take, we propose a methodology with
two approaches, one based on Deep Q-Learning and another one based on Genetic
Algorithms. The sequences of actions (confinement, self-isolation, two-meter
distance or not taking restrictions) are evaluated according to a reward system
focused on meeting two objectives: firstly, getting few people infected so that
hospitals are not overwhelmed with critical patients, and secondly, avoiding
taking drastic measures for too long which can potentially cause serious damage
to the economy. The conducted experiments prove that our methodology is a valid
tool to discover actions governments can take to reduce the negative effects of
a pandemic in both senses. We also prove that the approach based on Deep
Q-Learning overcomes the one based on Genetic Algorithms for optimizing the
sequences of actions
Patient Similarity Analysis with Longitudinal Health Data
Healthcare professionals have long envisioned using the enormous processing
powers of computers to discover new facts and medical knowledge locked inside
electronic health records. These vast medical archives contain time-resolved
information about medical visits, tests and procedures, as well as outcomes,
which together form individual patient journeys. By assessing the similarities
among these journeys, it is possible to uncover clusters of common disease
trajectories with shared health outcomes. The assignment of patient journeys to
specific clusters may in turn serve as the basis for personalized outcome
prediction and treatment selection. This procedure is a non-trivial
computational problem, as it requires the comparison of patient data with
multi-dimensional and multi-modal features that are captured at different times
and resolutions. In this review, we provide a comprehensive overview of the
tools and methods that are used in patient similarity analysis with
longitudinal data and discuss its potential for improving clinical decision
making
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data. Probabilistic
machine learning models help provide a complete picture of observed data in
healthcare. In this review, we examine how probabilistic machine learning can
advance healthcare. We consider challenges in the predictive model building
pipeline where probabilistic models can be beneficial including calibration and
missing data. Beyond predictive models, we also investigate the utility of
probabilistic machine learning models in phenotyping, in generative models for
clinical use cases, and in reinforcement learning.Comment: Annual Reviews of Biomedical Data Science 202
AI-oriented Medical Workload Allocation for Hierarchical Cloud/Edge/Device Computing
In a hierarchically-structured cloud/edge/device computing environment,
workload allocation can greatly affect the overall system performance. This
paper deals with AI-oriented medical workload generated in emergency rooms (ER)
or intensive care units (ICU) in metropolitan areas. The goal is to optimize
AI-workload allocation to cloud clusters, edge servers, and end devices so that
minimum response time can be achieved in life-saving emergency applications.
In particular, we developed a new workload allocation method for the AI
workload in distributed cloud/edge/device computing systems. An efficient
scheduling and allocation strategy is developed in order to reduce the overall
response time to satisfy multi-patient demands. We apply several ICU AI
workloads from a comprehensive edge computing benchmark Edge AIBench. The
healthcare AI applications involved are short-of-breath alerts, patient
phenotype classification, and life-death threats. Our experimental results
demonstrate the high efficiency and effectiveness in real-life health-care and
emergency applications
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