948 research outputs found
Combining Static and Dynamic Features for Multivariate Sequence Classification
Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another type of data. In real-life scenarios, however, it is often the case
that both static and dynamic features are present, or can be extracted from the
data. In this work, we demonstrate how generative models such as Hidden Markov
Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
be used to extract temporal information from the dynamic data. We explore how
the extracted information can be combined with the static features in order to
improve the classification performance. We evaluate the existing techniques and
suggest a hybrid approach, which outperforms other methods on several public
datasets.Comment: Presented at IEEE DSAA 201
Learning Better Clinical Risk Models.
Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models.
As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings.
Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd
Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Continual machine learning for non-stationary data analysis
Although deep learning models have achieved significant successes in various fields, most of them have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning changes, a conventional machine learning model will learn and adapt to the new status. However, the model will not remember or recognise any revisits to the previous states. This causes performance reduction and re-training curves in dealing with periodic or irregularly reoccurring changes in the data or goals. Without continual learning ability, one cannot deploy an adaptive machine learning model in a changing environment.
This thesis investigates the continual learning and mitigating the catastrophic forgetting problem in neural networks. We assume non-stationary data contains multiple different tasks which are coming in sequence and will not be stored. We propose a regularisation method, which is to identify and penalise the changes of important parameters of previous tasks while learning a new one. However, when the number of tasks is sufficiently large, this method cannot preserve all the previously learned knowledge, or it impedes the integration of new knowledge. This is also known as the stability-plasticity dilemma. To solve this problem, we proposed a replay method based on Generative Adversarial Networks (GANs). Different from other replay methods, the proposed model is not bounded by the fitting capacity of the generator. However, the number of parameters increases rapidly as the number of learned tasks grows. Therefore, we propose a continual learning model based on Bayesian neural networks and a Mixture of Experts (MoE) framework. The proposed model integrates different experts which are responsible for different tasks into a giant model. Previously knowledge is preserved, and new tasks can be efficiently learned by assigning new experts. Based on Monte-Carlo Sampling, the performance is not satisfied. To address this issue, we propose a Probabilistic Neural Network (PNN) and integrate it with a conventional neural network. The PNN can produce the likelihood given input and be used in a variety of fields. To apply continual learning methods to real-world applications, we then propose a semi-supervised learning model to analyse healthcare datasets. The proposed framework extracts the general features from unlabelled data. We integrate the PNN into the framework to classify the data, which includes a smaller set of labelled samples and continually learn the new cases.
The proposed model has been tested on benchmark datasets and also a real-world clinical dataset. The results showed that our proposed model outperforms the state-of-the-art models without requiring prior knowledge of the tasks and overall accuracy of the continual learning. The experiments on the real-world clinical data were designed to identify the risk of Urinary Tract Infections (UTIs) using in-home monitoring data. The UTI risk analysis model has been deployed in a digital platform and is currently part of the on-going Minder clinical study at the UK Dementia Research Institute (UK DRI). An earlier version of the model was deployed as a part of a Class-I CE marked medical device. The UK DRI Minder platform and the deployed machine learning models, including the UTI risk analysis model developed in this research, are in the process to be accredited as a Class-IIa medical device.
Overall, this PhD research tackles theoretical and applied challenges of continuous learning models in dealing with real-world data. We evaluate the proposed continual learning methods in a variety of benchmarks with comprehensive analysis and show their effectiveness. Furthermore, we have applied the proposed methods in real-world applications and demonstrated the applicability of the models to real-world settings and clinical problems.Open Acces
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research
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