5,499 research outputs found
Probabilistic classification of acute myocardial infarction from multiple cardiac markers
Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78–0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1–6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI
A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
We propose a novel Coupled Hidden Markov Model to detect epileptic seizures
in multichannel electroencephalography (EEG) data. Our model defines a network
of seizure propagation paths to capture both the temporal and spatial evolution
of epileptic activity. To address the intractability introduced by the coupled
interactions, we derive a variational inference procedure to efficiently infer
the seizure evolution from spectral patterns in the EEG data. We validate our
model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit
of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate
that our model outperforms three baseline approaches which rely on a classical
detection framework. Our model also demonstrates the potential to localize
seizure onset zones in focal epilepsy.Comment: To appear in MICCAI 2018 Proceeding
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
In this paper, the problem of road friction prediction from a fleet of
connected vehicles is investigated. A framework is proposed to predict the road
friction level using both historical friction data from the connected cars and
data from weather stations, and comparative results from different methods are
presented. The problem is formulated as a classification task where the
available data is used to train three machine learning models including
logistic regression, support vector machine, and neural networks to predict the
friction class (slippery or non-slippery) in the future for specific road
segments. In addition to the friction values, which are measured by moving
vehicles, additional parameters such as humidity, temperature, and rainfall are
used to obtain a set of descriptive feature vectors as input to the
classification methods. The proposed prediction models are evaluated for
different prediction horizons (0 to 120 minutes in the future) where the
evaluation shows that the neural networks method leads to more stable results
in different conditions.Comment: Published at IV 201
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
In many fields of science, generalized likelihood ratio tests are established
tools for statistical inference. At the same time, it has become increasingly
common that a simulator (or generative model) is used to describe complex
processes that tie parameters of an underlying theory and measurement
apparatus to high-dimensional observations .
However, simulator often do not provide a way to evaluate the likelihood
function for a given observation , which motivates a new class of
likelihood-free inference algorithms. In this paper, we show that likelihood
ratios are invariant under a specific class of dimensionality reduction maps
. As a direct consequence, we show that
discriminative classifiers can be used to approximate the generalized
likelihood ratio statistic when only a generative model for the data is
available. This leads to a new machine learning-based approach to
likelihood-free inference that is complementary to Approximate Bayesian
Computation, and which does not require a prior on the model parameters.
Experimental results on artificial problems with known exact likelihoods
illustrate the potential of the proposed method.Comment: 35 pages, 5 figure
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