26,539 research outputs found
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
MASK: A flexible framework to facilitate de-identification of clinical texts
Medical health records and clinical summaries contain a vast amount of
important information in textual form that can help advancing research on
treatments, drugs and public health. However, the majority of these information
is not shared because they contain private information about patients, their
families, or medical staff treating them. Regulations such as HIPPA in the US,
PHIPPA in Canada and GDPR regulate the protection, processing and distribution
of this information. In case this information is de-identified and personal
information are replaced or redacted, they could be distributed to the research
community. In this paper, we present MASK, a software package that is designed
to perform the de-identification task. The software is able to perform named
entity recognition using some of the state-of-the-art techniques and then mask
or redact recognized entities. The user is able to select named entity
recognition algorithm (currently implemented are two versions of CRF-based
techniques and BiLSTM-based neural network with pre-trained GLoVe and ELMo
embedding) and masking algorithm (e.g. shift dates, replace names/locations,
totally redact entity)
Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We assess the performance of deep learning algorithms and
compare them with classical NLP approaches.
Materials and Methods: We compare convolutional neural networks (CNNs),
n-gram models, and approaches based on cTAKES that extract pre-defined medical
concepts from clinical notes and use them to predict patient phenotypes. The
performance is tested on 10 different phenotyping tasks using 1,610 discharge
summaries extracted from the MIMIC-III database.
Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The
average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our
model having an F1-score up to 37 points higher than alternative approaches. We
additionally assess the interpretability of our model by presenting a method
that extracts the most salient phrases for a particular prediction.
Conclusion: We show that NLP methods based on deep learning improve the
performance of patient phenotyping. Our CNN-based algorithm automatically
learns the phrases associated with each patient phenotype. As such, it reduces
the annotation complexity for clinical domain experts, who are normally
required to develop task-specific annotation rules and identify relevant
phrases. Our method performs well in terms of both performance and
interpretability, which indicates that deep learning is an effective approach
to patient phenotyping based on clinicians' notes
De-identification of medical records using conditional random fields and long short-term memory networks
The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing
focuses on the de-identification of psychiatric evaluation records. This paper
describes two participating systems of our team, based on conditional random
fields (CRFs) and long short-term memory networks (LSTMs). A pre-processing
module was introduced for sentence detection and tokenization before
de-identification. For CRFs, manually extracted rich features were utilized to
train the model. For LSTMs, a character-level bi-directional LSTM network was
applied to represent tokens and classify tags for each token, following which a
decoding layer was stacked to decode the most probable protected health
information (PHI) terms. The LSTM-based system attained an i2b2 strict
micro-F_1 measure of 89.86%, which was higher than that of the CRF-based
system
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational
advances in medical research. However, various concerns, particularly over
privacy, can limit access to and collaborative use of EHR data. Sharing
synthetic EHR data could mitigate risk. In this paper, we propose a new
approach, medical Generative Adversarial Network (medGAN), to generate
realistic synthetic patient records. Based on input real patient records,
medGAN can generate high-dimensional discrete variables (e.g., binary and count
features) via a combination of an autoencoder and generative adversarial
networks. We also propose minibatch averaging to efficiently avoid mode
collapse, and increase the learning efficiency with batch normalization and
shortcut connections. To demonstrate feasibility, we showed that medGAN
generates synthetic patient records that achieve comparable performance to real
data on many experiments including distribution statistics, predictive modeling
tasks and a medical expert review. We also empirically observe a limited
privacy risk in both identity and attribute disclosure using medGAN.Comment: Accepted at Machine Learning in Health Care (MLHC) 201
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the
rapid loss of kidney excretory function, which aggravates the clinical severity
of other diseases in a large number of hospitalized patients. Accurate early
prediction of AKI can enable in-time interventions and treatments. However, AKI
is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to
an improved understanding of the disease pathophysiology and development of
more targeted clinical interventions. This study used a memory network-based
deep learning approach to discover AKI sub-phenotypes using structured and
unstructured electronic health record (EHR) data of patients before AKI
diagnosis. We leveraged a real world critical care EHR corpus including 37,486
ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype
I is with an average age of 63.03 years, and is characterized by
mild loss of kidney excretory function (Serum Creatinine (SCr)
mg/dL, estimated Glomerular Filtration Rate Test (eGFR)
mL/min/1.73). These patients are more likely to develop stage I AKI.
Sub-phenotype II is with average age 66.81 years, and was
characterized by severe loss of kidney excretory function (SCr
mg/dL, eGFR mL/min/1.73). These patients are more likely
to develop stage III AKI. Sub-phenotype III is with average age 65.07 years, and was characterized moderate loss of kidney excretory function
and thus more likely to develop stage II AKI (SCr mg/dL, eGFR
mL/min/1.73). Both SCr and eGFR are significantly
different across the three sub-phenotypes with statistical testing plus postdoc
analysis, and the conclusion still holds after age adjustment
Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks
The recent advances in ECG sensor devices provide opportunities for user
self-managed auto-diagnosis and monitoring services over the internet. This
imposes the requirements for generic ECG classification methods that are
inter-patient and device independent. In this paper, we present our work on
using the densely connected convolutional neural network (DenseNet) and gated
recurrent unit network (GRU) for addressing the inter-patient ECG
classification problem. A deep learning model architecture is proposed and is
evaluated using the MIT-BIH Arrhythmia and Supraventricular Databases. The
results obtained show that without applying any complicated data pre-processing
or feature engineering methods, both of our models have considerably
outperformed the state-of-the-art performance for supraventricular (SVEB) and
ventricular (VEB) arrhythmia classifications on the unseen testing dataset
(with the F1 score improved from 51.08 to 61.25 for SVEB detection and from
88.59 to 89.75 for VEB detection respectively). As no patient-specific or
device-specific information is used at the training stage in this work, it can
be considered as a more generic approach for dealing with scenarios in which
varieties of ECG signals are collected from different patients using different
types of sensor devices.Comment: 10 pages, 8 figure
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Scalable and accurate deep learning with electronic health records.
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset
Communication of follow-up recommendations when abnormalities are identified
on imaging studies is prone to error. In this paper, we present a natural
language processing approach based on deep learning to automatically identify
clinically important recommendations in radiology reports. Our approach first
identifies the recommendation sentences and then extracts reason, test, and
time frame of the identified recommendations. To train our extraction models,
we created a corpus of 567 radiology reports annotated for recommendation
information. Our extraction models achieved 0.92 f-score for recommendation
sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for
time frame. We applied the extraction models to a set of over 3.3 million
radiology reports and analyzed the adherence of follow-up recommendations.Comment: Under Review at American Medical Informatics Association Fall
Symposium'201
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study
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 paper, we propose a predictive
modeling approach based on deep learning based 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
in 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
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