5,781 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
For recurrent neural networks trained on time series with target and
exogenous variables, in addition to accurate prediction, it is also desired to
provide interpretable insights into the data. In this paper, we explore the
structure of LSTM recurrent neural networks to learn variable-wise hidden
states, with the aim to capture different dynamics in multi-variable time
series and distinguish the contribution of variables to the prediction. With
these variable-wise hidden states, a mixture attention mechanism is proposed to
model the generative process of the target. Then we develop associated training
methods to jointly learn network parameters, variable and temporal importance
w.r.t the prediction of the target variable. Extensive experiments on real
datasets demonstrate enhanced prediction performance by capturing the dynamics
of different variables. Meanwhile, we evaluate the interpretation results both
qualitatively and quantitatively. It exhibits the prospect as an end-to-end
framework for both forecasting and knowledge extraction over multi-variable
data.Comment: Accepted to International Conference on Machine Learning (ICML), 201
Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction
Building models for health prediction based on Electronic Health Records
(EHR) has become an active research area. EHR patient journey data consists of
patient time-ordered clinical events/visits from patients. Most existing
studies focus on modeling long-term dependencies between visits, without
explicitly taking short-term correlations between consecutive visits into
account, where irregular time intervals, incorporated as auxiliary information,
are fed into health prediction models to capture latent progressive patterns of
patient journeys. We present a novel deep neural network with four modules to
take into account the contributions of various variables for health prediction:
i) the Stacked Attention module strengthens the deep semantics in clinical
events within each patient journey and generates visit embeddings, ii) the
Short-Term Temporal Attention module models short-term correlations between
consecutive visit embeddings while capturing the impact of time intervals
within those visit embeddings, iii) the Long-Term Temporal Attention module
models long-term dependencies between visit embeddings while capturing the
impact of time intervals within those visit embeddings, iv) and finally, the
Coupled Attention module adaptively aggregates the outputs of Short-Term
Temporal Attention and Long-Term Temporal Attention modules to make health
predictions. Experimental results on MIMIC-III demonstrate superior predictive
accuracy of our model compared to existing state-of-the-art methods, as well as
the interpretability and robustness of this approach. Furthermore, we found
that modeling short-term correlations contributes to local priors generation,
leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202
Secondary use of Structured Electronic Health Records Data: From Observational Studies to Deep Learning-based Predictive Modeling
With the wide adoption of electronic health records (EHRs), researchers, as well as large healthcare organizations, governmental institutions, insurance, and pharmaceutical companies have been interested in leveraging this rich clinical data source to extract clinical evidence and develop predictive algorithms. Large vendors have been able to compile structured EHR data from sites all over the United States, de-identify these data, and make them available to data science researchers in a more usable format. For this dissertation, we leveraged one of the earliest and largest secondary EHR data sources and conducted three studies of increasing scope. In the first study, which was of limited scope, we conducted a retrospective observational study to compare the effect of three drugs on a specific population of approximately 3,000 patients. Using a novel statistical method, we found evidence that the selection of phenylephrine as the primary vasopressor to induce hypertension for the management of nontraumatic subarachnoid hemorrhage is associated with better outcomes as compared to selecting norepinephrine or dopamine. In the second study, we widened our scope, using a cohort of more than 100,000 patients to train generalizable models for the risk prediction of specific clinical events, such as heart failure in diabetes patients or pancreatic cancer. In this study, we found that recurrent neural network-based predictive models trained on expressive terminologies, which preserve a high level of granularity, are associated with better prediction performance as compared with other baseline methods, such as logistic regression. Finally, we widened our scope again, to train Med-BERT, a foundation model, on more than 20 million patients’ diagnosis data. Med-BERT was found to improve the prediction performance of downstream tasks that have a small sample size, which otherwise would limit the ability of the model to learn good representation. In conclusion, we found that we can extract useful information and train helpful deep learning-based predictive models. Given the limitations of secondary EHR data and taking into consideration that the data were originally collected for administrative and not research purposes, however, the findings need clinical validation. Therefore, clinical trials are warranted to further validate any new evidence extracted from such data sources before updating clinical practice guidelines. The implementability of the developed predictive models, which are in an early development phase, also warrants further evaluation
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.Comment: To appear in IJC
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