505 research outputs found
Semi-Supervised Variational Autoencoder for Survival Prediction
In this paper we propose a semi-supervised variational autoencoder for
classification of overall survival groups from tumor segmentation masks. The
model can use the output of any tumor segmentation algorithm, removing all
assumptions on the scanning platform and the specific type of pulse sequences
used, thereby increasing its generalization properties. Due to its
semi-supervised nature, the method can learn to classify survival time by using
a relatively small number of labeled subjects. We validate our model on the
publicly available dataset from the Multimodal Brain Tumor Segmentation
Challenge (BraTS) 2019.Comment: Published in the pre-conference proceeding of "2019 International
MICCAI BraTS Challenge
Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare
Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. There is a noticeable opportunity to implement a learning health care system and data-driven healthcare to make better medical decisions, better personalized predictions; and more precise discovering of risk factors and their interactions. In this research we focus on data-driven approaches for personalized medicine. We propose a research framework which emphasizes on three main phases: 1) Predictive modeling, 2) Patient subgroup analysis and 3) Treatment recommendation. Our goal is to develop novel methods for each phase and apply them in real-world applications.
In the fist phase, we develop a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures (Stacked autoencoders, Deep belief network and Variational autoencoders) for feature representation in higher-level abstractions to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. We perform our method on different small and large datasets. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others.
In the second phase, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables.
Finally, in the third phase, we introduce a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we propose a simple yet effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models
Representation Learning With Autoencoders For Electronic Health Records
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however
is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this research, we propose a predictive modeling approach based on deep feature representations and word embedding techniques. Our method uses different deep architectures (stacked sparse autoencoders, deep belief network, adversarial autoencoders and variational autoencoders) for feature representation in higher-level abstraction to obtain effective and robust features from EHRs, and then build prediction models on top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled data is scarce. We investigate the performance of representation learning through a supervised learning approach. Our focus is to present a comparative study to evaluate the performance of different deep architectures through supervised learning and provide insights for the choice of deep feature representation techniques. Our experiments demonstrate that for small data sets, stacked sparse autoencoder demonstrates a superior generality performance in prediction due to sparsity regularization whereas variational autoencoders outperform the competing approaches for large data sets due to its capability of learning the representation distribution
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
The interpretation of complex high-dimensional data typically requires the
use of dimensionality reduction techniques to extract explanatory
low-dimensional representations. However, in many real-world problems these
representations may not be sufficient to aid interpretation on their own, and
it would be desirable to interpret the model in terms of the original features
themselves. Our goal is to characterise how feature-level variation depends on
latent low-dimensional representations, external covariates, and non-linear
interactions between the two. In this paper, we propose to achieve this through
a structured kernel decomposition in a hybrid Gaussian Process model which we
call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We
demonstrate the utility of our model on simulated examples and applications in
disease progression modelling from high-dimensional gene expression data in the
presence of additional phenotypes. In each setting we show how the c-GPLVM can
extract low-dimensional structures from high-dimensional data sets whilst
allowing a breakdown of feature-level variability that is not present in other
commonly used dimensionality reduction approaches
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting
modality for COVID-19 patients who are refractory to conventional therapies.
However, the proper treatment decision has been the subject of significant
debate and it remains controversial about who benefits from this scarcely
available and technically complex treatment option. To support clinical
decisions, it is a critical need to predict the treatment need and the
potential treatment and no-treatment responses. Targeting this clinical
challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel
approach for individualized treatment analysis. TVAE is specifically designed
to address the modeling challenges like ECMO with strong treatment selection
bias and scarce treatment cases. TVAE conceptualizes the treatment decision as
a multi-scale problem. We model a patient's potential treatment assignment and
the factual and counterfactual outcomes as part of their intrinsic
characteristics that can be represented by a deep latent variable model. The
factual and counterfactual prediction errors are alleviated via a
reconstruction regularization scheme together with semi-supervision, and the
selection bias and the scarcity of treatment cases are mitigated by the
disentangled and distribution-matched latent space and the label-balancing
generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an
international dataset collected from 1651 hospitals across 63 countries, and a
institutional dataset collected from 15 hospitals. The results show that TVAE
outperforms state-of-the-art treatment effect models in predicting both the
propensity scores and factual outcomes on heterogeneous COVID-19 datasets.
Additional experiments also show TVAE outperforms the best existing models in
individual treatment effect estimation on the synthesized IHDP benchmark
dataset
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