93 research outputs found
Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis
AbstractâLate onset neonatal sepsis is one of the major clinical concerns when premature babies receive intensive care. Current practice relies on slow laboratory testing of blood cultures for diagnosis. A valuable research question is whether sepsis can be reliably detected before the blood sample is taken. This paper investigates the extent to which physiological events observed in the patientâs monitoring traces could be used for the early detec-tion of neonatal sepsis. We model the distribution of these events with an autoregressive hidden Markov model (AR-HMM).Both learning and inference carefully use domain knowledge to extract the babyâs true physiology from the monitoring data. Our model can produce real-time predictions about the onset of the infection and also handles missing data. We evaluate the effectiveness of the AR-HMM for sepsis detection on a dataset collected from the Neonatal Intensive Care Unit (NICU) at the Royal Infirmary of Edinburgh. Index Termsâneonatal sepsis, autoregressive hidden Markov model, real-time inference, intensive care. I
Dynamical models for neonatal intensive care monitoring
The vital signs monitoring data of an infant receiving intensive care are a rich source of information
about its health condition. One major concern about the state of health of such patients
is the onset of neonatal sepsis, a life-threatening bloodstream infection. As early signs are subtle
and current diagnosis procedures involve slow laboratory testing, sepsis detection based on
the monitored physiological dynamics is a clinically significant task. This challenging problem
can be thoroughly modelled as real-time inference within a machine learning framework.
In this thesis, we develop probabilistic dynamical models centred around the goal of providing
useful predictions about the onset of neonatal sepsis. This research is characterised by
the careful incorporation of domain knowledge for the purpose of extracting the infantâs true
physiology from the monitoring data.
We make two main contributions. The first one is the formulation of sepsis detection as
learning and inference in an Auto-Regressive Hidden Markov Model (AR-HMM). The model
investigates the extent to which physiological events observed in the patientâs monitoring traces
could be used for the early detection of neonatal sepsis. In addition, the proposed approach
involves exact marginalisation over missing data at inference time. When applying the ARHMM
on a real-world dataset, we found that it can produce effective predictions about the
onset of sepsis.
Second, both sepsis and clinical event detection are formulated as learning and inference in
a Hierarchical Switching Linear Dynamical System (HSLDS). The HSLDS models dynamical
systems where complex interactions between modes of operation can be represented as a twolevel
hidden discrete hierarchical structure. For neonatal condition monitoring, the lower layer
models clinical events and is controlled by upper layer variables with semantics sepsis/nonsepsis.
The model parameterisation and estimation procedures are adapted to the specifics of
physiological monitoring data. We demonstrate that the performance of the HSLDS for the
detection of sepsis is not statistically different from the AR-HMM, despite the fact that the
latter model is given âground truthâ annotations of the patientâs physiology
PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS
Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease
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Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.
IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454
Malware Classification with GMM-HMM Models
Discrete hidden Markov models (HMM) are often applied to malware detection
and classification problems. However, the continuous analog of discrete HMMs,
that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the
field of cybersecurity. In this paper, we use GMM-HMMs for malware
classification and we compare our results to those obtained using discrete
HMMs. As features, we consider opcode sequences and entropy-based sequences.
For our opcode features, GMM-HMMs produce results that are comparable to those
obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs
generally improve significantly on the classification results that we have
achieved with discrete HMMs
Adaptive, locally-linear models of complex dynamics
The dynamics of complex systems generally include high-dimensional,
non-stationary and non-linear behavior, all of which pose fundamental
challenges to quantitative understanding. To address these difficulties we
detail a new approach based on local linear models within windows determined
adaptively from the data. While the dynamics within each window are simple,
consisting of exponential decay, growth and oscillations, the collection of
local parameters across all windows provides a principled characterization of
the full time series. To explore the resulting model space, we develop a novel
likelihood-based hierarchical clustering and we examine the eigenvalues of the
linear dynamics. We demonstrate our analysis with the Lorenz system undergoing
stable spiral dynamics and in the standard chaotic regime. Applied to the
posture dynamics of the nematode our approach identifies
fine-grained behavioral states and model dynamics which fluctuate close to an
instability boundary, and we detail a bifurcation in a transition from forward
to backward crawling. Finally, we analyze whole-brain imaging in
and show that the stability of global brain states changes with oxygen
concentration.Comment: 25 pages, 16 figure
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