17,882 research outputs found
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
Deep neural networks have shown promising results for various clinical
prediction tasks such as diagnosis, mortality prediction, predicting duration
of stay in hospital, etc. However, training deep networks -- such as those
based on Recurrent Neural Networks (RNNs) -- requires large labeled data, high
computational resources, and significant hyperparameter tuning effort. In this
work, we investigate as to what extent can transfer learning address these
issues when using deep RNNs to model multivariate clinical time series. We
consider transferring the knowledge captured in an RNN trained on several
source tasks simultaneously using a large labeled dataset to build the model
for a target task with limited labeled data. An RNN pre-trained on several
tasks provides generic features, which are then used to build simpler linear
models for new target tasks without training task-specific RNNs. For
evaluation, we train a deep RNN to identify several patient phenotypes on time
series from MIMIC-III database, and then use the features extracted using that
RNN to build classifiers for identifying previously unseen phenotypes, and also
for a seemingly unrelated task of in-hospital mortality. We demonstrate that
(i) models trained on features extracted using pre-trained RNN outperform or,
in the worst case, perform as well as task-specific RNNs; (ii) the models using
features from pre-trained models are more robust to the size of labeled data
than task-specific RNNs; and (iii) features extracted using pre-trained RNN are
generic enough and perform better than typical statistical hand-crafted
features.Comment: Accepted at Machine Learning for Medicine and Healthcare Workshop at
ACM KDD 2018 Conferenc
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
Deep neural networks have shown promising results for various clinical
prediction tasks. However, training deep networks such as those based on
Recurrent Neural Networks (RNNs) requires large labeled data, significant
hyper-parameter tuning effort and expertise, and high computational resources.
In this work, we investigate as to what extent can transfer learning address
these issues when using deep RNNs to model multivariate clinical time series.
We consider two scenarios for transfer learning using RNNs: i)
domain-adaptation, i.e., leveraging a deep RNN - namely, TimeNet - pre-trained
for feature extraction on time series from diverse domains, and adapting it for
feature extraction and subsequent target tasks in healthcare domain, ii)
task-adaptation, i.e., pre-training a deep RNN - namely, HealthNet - on diverse
tasks in healthcare domain, and adapting it to new target tasks in the same
domain. We evaluate the above approaches on publicly available MIMIC-III
benchmark dataset, and demonstrate that (a) computationally-efficient linear
models trained using features extracted via pre-trained RNNs outperform or, in
the worst case, perform as well as deep RNNs and statistical hand-crafted
features based models trained specifically for target task; (b) models obtained
by adapting pre-trained models for target tasks are significantly more robust
to the size of labeled data compared to task-specific RNNs, while also being
computationally efficient. We, therefore, conclude that pre-trained deep models
like TimeNet and HealthNet allow leveraging the advantages of deep learning for
clinical time series analysis tasks, while also minimize dependence on
hand-crafted features, deal robustly with scarce labeled training data
scenarios without overfitting, as well as reduce dependence on expertise and
resources required to train deep networks from scratch.Comment: Updated version of this work appeared in Journal of Healthcare
Informatics Research, Vol. 4, 2020. arXiv admin note: text overlap with
arXiv:1807.0170
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
The past decade has seen an explosion in the amount of digital information
stored in electronic health records (EHR). While primarily designed for
archiving patient clinical information and administrative healthcare tasks,
many researchers have found secondary use of these records for various clinical
informatics tasks. Over the same period, the machine learning community has
seen widespread advances in deep learning techniques, which also have been
successfully applied to the vast amount of EHR data. In this paper, we review
these deep EHR systems, examining architectures, technical aspects, and
clinical applications. We also identify shortcomings of current techniques and
discuss avenues of future research for EHR-based deep learning.Comment: Accepted for publication with Journal of Biomedical and Health
Informatics: http://ieeexplore.ieee.org/abstract/document/8086133
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks
Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is
difficult since many cases of AF are usually clinically silent and undiagnosed.
In particular paroxysmal AF is a form of AF that occurs occasionally, and has a
higher probability of being undetected. In this work, we present an attention
based deep learning framework for detection of paroxysmal AF episodes from a
sequence of windows. Time-frequency representation of 30 seconds recording
windows, over a 10 minute data segment, are fed sequentially into a deep
convolutional neural network for image-based feature extraction, which are then
presented to a bidirectional recurrent neural network with an attention layer
for AF detection. To demonstrate the effectiveness of the proposed framework
for transient AF detection, we use a database of 24 hour Holter
Electrocardiogram (ECG) recordings acquired from 2850 patients at the
University of Virginia heart station. The algorithm achieves an AUC of 0.94 on
the testing set, which exceeds the performance of baseline models. We also
demonstrate the cross-domain generalizablity of the approach by adapting the
learned model parameters from one recording modality (ECG) to another
(photoplethysmogram) with improved AF detection performance. The proposed high
accuracy, low false alarm algorithm for detecting paroxysmal AF has potential
applications in long-term monitoring using wearable sensors.Comment: Accepted to the 24th ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining (KDD 2018), London, UK, 201
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Objective: To compare different deep learning architectures for predicting
the risk of readmission within 30 days of discharge from the intensive care
unit (ICU). The interpretability of attention-based models is leveraged to
describe patients-at-risk. Methods: Several deep learning architectures making
use of attention mechanisms, recurrent layers, neural ordinary differential
equations (ODEs), and medical concept embeddings with time-aware attention were
trained using publicly available electronic medical record data (MIMIC-III)
associated with 45,298 ICU stays for 33,150 patients. Bayesian inference was
used to compute the posterior over weights of an attention-based model. Odds
ratios associated with an increased risk of readmission were computed for
static variables. Diagnoses, procedures, medications, and vital signs were
ranked according to the associated risk of readmission. Results: A recurrent
neural network, with time dynamics of code embeddings computed by neural ODEs,
achieved the highest average precision of 0.331 (AUROC: 0.739, F1-Score:
0.372). Predictive accuracy was comparable across neural network architectures.
Groups of patients at risk included those suffering from infectious
complications, with chronic or progressive conditions, and for whom standard
medical care was not suitable. Conclusions: Attention-based networks may be
preferable to recurrent networks if an interpretable model is required, at only
marginal cost in predictive accuracy
Effective Representations of Clinical Notes
Clinical notes are a rich source of information about patient state. However,
using them to predict clinical events with machine learning models is
challenging. They are very high dimensional, sparse and have complex structure.
Furthermore, training data is often scarce because it is expensive to obtain
reliable labels for many clinical events. These difficulties have traditionally
been addressed by manual feature engineering encoding task specific domain
knowledge. We explored the use of neural networks and transfer learning to
learn representations of clinical notes that are useful for predicting future
clinical events of interest, such as all causes mortality, inpatient
admissions, and emergency room visits. Our data comprised 2.7 million notes and
115 thousand patients at Stanford Hospital. We used the learned
representations, along with commonly used bag of words and topic model
representations, as features for predictive models of clinical events. We
evaluated the effectiveness of these representations with respect to the
performance of the models trained on small datasets. Models using the neural
network derived representations performed significantly better than models
using the baseline representations with small () training datasets.
The learned representations offer significant performance gains over commonly
used baseline representations for a range of predictive modeling tasks and
cohort sizes, offering an effective alternative to task specific feature
engineering when plentiful labeled training data is not available
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages, such as
structural magnetic resonance imaging (MRI), functional MRI, and positron
emission tomography (PET), and has achieved significant performance
improvements over traditional machine learning in computer-aided diagnosis of
brain disorders. This paper reviews the applications of deep learning methods
for neuroimaging-based brain disorder analysis. We first provide a
comprehensive overview of deep learning techniques and popular network
architectures, by introducing various types of deep neural networks and recent
developments. We then review deep learning methods for computer-aided analysis
of four typical brain disorders, including Alzheimer's disease, Parkinson's
disease, Autism spectrum disorder, and Schizophrenia, where the first two
diseases are neurodegenerative disorders and the last two are
neurodevelopmental and psychiatric disorders, respectively. More importantly,
we discuss the limitations of existing studies and present possible future
directions.Comment: 30 pages, 7 figure
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Leveraging large historical data in electronic health record (EHR), we
developed Doctor AI, a generic predictive model that covers observed medical
conditions and medication uses. Doctor AI is a temporal model using recurrent
neural networks (RNN) and was developed and applied to longitudinal time
stamped EHR data from 260K patients over 8 years. Encounter records (e.g.
diagnosis codes, medication codes or procedure codes) were input to RNN to
predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one
label for each diagnosis or medication category). Based on separate blind test
set evaluation, Doctor AI can perform differential diagnosis with up to 79%
recall@30, significantly higher than several baselines. Moreover, we
demonstrate great generalizability of Doctor AI by adapting the resulting
models from one institution to another without losing substantial accuracy.Comment: Presented at 2016 Machine Learning and Healthcare Conference (MLHC
2016), Los Angeles, C
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
Training deep neural networks often requires careful hyper-parameter tuning
and significant computational resources. In this paper, we propose ConvTimeNet
(CTN): an off-the-shelf deep convolutional neural network (CNN) trained on
diverse univariate time series classification (TSC) source tasks. Once trained,
CTN can be easily adapted to new TSC target tasks via a small amount of
fine-tuning using labeled instances from the target tasks. We note that the
length of convolutional filters is a key aspect when building a pre-trained
model that can generalize to time series of different lengths across datasets.
To achieve this, we incorporate filters of multiple lengths in all
convolutional layers of CTN to capture temporal features at multiple time
scales. We consider all 65 datasets with time series of lengths up to 512
points from the UCR TSC Benchmark for training and testing transferability of
CTN: We train CTN on a randomly chosen subset of 24 datasets using a multi-head
approach with a different softmax layer for each training dataset, and study
generalizability and transferability of the learned filters on the remaining 41
TSC datasets. We observe significant gains in classification accuracy as well
as computational efficiency when using pre-trained CTN as a starting point for
subsequent task-specific fine-tuning compared to existing state-of-the-art TSC
approaches. We also provide qualitative insights into the working of CTN by: i)
analyzing the activations and filters of first convolution layer suggesting the
filters in CTN are generically useful, ii) analyzing the impact of the design
decision to incorporate multiple length decisions, and iii) finding regions of
time series that affect the final classification decision via occlusion
sensitivity analysis.Comment: Accepted at IJCNN 201
An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes
Background and Objective: Code assignment is of paramount importance in many
levels in modern hospitals, from ensuring accurate billing process to creating
a valid record of patient care history. However, the coding process is tedious
and subjective, and it requires medical coders with extensive training. This
study aims to evaluate the performance of deep-learning-based systems to
automatically map clinical notes to ICD-9 medical codes. Methods: The
evaluations of this research are focused on end-to-end learning methods without
manually defined rules. Traditional machine learning algorithms, as well as
state-of-the-art deep learning methods such as Recurrent Neural Networks and
Convolution Neural Networks, were applied to the Medical Information Mart for
Intensive Care (MIMIC-III) dataset. An extensive number of experiments was
applied to different settings of the tested algorithm. Results: Findings showed
that the deep learning-based methods outperformed other conventional machine
learning methods. From our assessment, the best models could predict the top 10
ICD-9 codes with 0.6957 F1 and 0.8967 accuracy and could estimate the top 10
ICD-9 categories with 0.7233 F1 and 0.8588 accuracy. Our implementation also
outperformed existing work under certain evaluation metrics. Conclusion: A set
of standard metrics was utilized in assessing the performance of ICD-9 code
assignment on MIMIC-III dataset. All the developed evaluation tools and
resources are available online, which can be used as a baseline for further
research
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