42,206 research outputs found
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients
within 30 days of discharge is important because such readmissions are not only
expensive but also critical indicator of provider care and quality of
treatment. Accurately predicting the risk-of-readmission may allow hospitals to
identify high-risk patients and eventually improve quality of care by
identifying factors that contribute to such readmissions in many scenarios. In
this paper, we investigate the problem of predicting risk-of-readmission as a
supervised learning problem, using a multi-layer classification approach.
Earlier contributions inadequately attempted to assess a risk value for 30 day
readmission by building a direct predictive model as opposed to our approach.
We first split the problem into various stages, (a) at risk in general (b) risk
within 60 days (c) risk within 30 days, and then build suitable classifiers for
each stage, thereby increasing the ability to accurately predict the risk using
multiple layers of decision. The advantage of our approach is that we can use
different classification models for the subtasks that are more suited for the
respective problems. Moreover, each of the subtasks can be solved using
different features and training data leading to a highly confident diagnosis or
risk compared to a one-shot single layer approach. An experimental evaluation
on actual hospital patient record data from Multicare Health Systems shows that
our model is significantly better at predicting risk-of-readmission of CHF
patients within 30 days after discharge compared to prior attempts
Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care
Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in
multiple complications. Risk prediction and profiling of T2DM complications is
critical for healthcare professionals to design personalized treatment plans
for patients in diabetes care for improved outcomes. In this paper, we study
the risk of developing complications after the initial T2DM diagnosis from
longitudinal patient records. We propose a novel multi-task learning approach
to simultaneously model multiple complications where each task corresponds to
the risk modeling of one complication. Specifically, the proposed method
strategically captures the relationships (1) between the risks of multiple T2DM
complications, (2) between the different risk factors, and (3) between the risk
factor selection patterns. The method uses coefficient shrinkage to identify an
informative subset of risk factors from high-dimensional data, and uses a
hierarchical Bayesian framework to allow domain knowledge to be incorporated as
priors. The proposed method is favorable for healthcare applications because in
additional to improved prediction performance, relationships among the
different risks and risk factors are also identified. Extensive experimental
results on a large electronic medical claims database show that the proposed
method outperforms state-of-the-art models by a significant margin.
Furthermore, we show that the risk associations learned and the risk factors
identified lead to meaningful clinical insights
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Deep learning models exhibit state-of-the-art performance for many predictive
healthcare tasks using electronic health records (EHR) data, but these models
typically require training data volume that exceeds the capacity of most
healthcare systems. External resources such as medical ontologies are used to
bridge the data volume constraint, but this approach is often not directly
applicable or useful because of inconsistencies with terminology. To solve the
data insufficiency challenge, we leverage the inherent multilevel structure of
EHR data and, in particular, the encoded relationships among medical codes. We
propose Multilevel Medical Embedding (MiME) which learns the multilevel
embedding of EHR data while jointly performing auxiliary prediction tasks that
rely on this inherent EHR structure without the need for external labels. We
conducted two prediction tasks, heart failure prediction and sequential disease
prediction, where MiME outperformed baseline methods in diverse evaluation
settings. In particular, MiME consistently outperformed all baselines when
predicting heart failure on datasets of different volumes, especially
demonstrating the greatest performance improvement (15% relative gain in PR-AUC
over the best baseline) on the smallest dataset, demonstrating its ability to
effectively model the multilevel structure of EHR data.Comment: Accepted at NIPS 201
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
We have recently seen many successful applications of recurrent neural
networks (RNNs) on electronic medical records (EMRs), which contain histories
of patients' diagnoses, medications, and other various events, in order to
predict the current and future states of patients. Despite the strong
performance of RNNs, it is often challenging for users to understand why the
model makes a particular prediction. Such black-box nature of RNNs can impede
its wide adoption in clinical practice. Furthermore, we have no established
methods to interactively leverage users' domain expertise and prior knowledge
as inputs for steering the model. Therefore, our design study aims to provide a
visual analytics solution to increase interpretability and interactivity of
RNNs via a joint effort of medical experts, artificial intelligence scientists,
and visual analytics researchers. Following the iterative design process
between the experts, we design, implement, and evaluate a visual analytics tool
called RetainVis, which couples a newly improved, interpretable and interactive
RNN-based model called RetainEX and visualizations for users' exploration of
EMR data in the context of prediction tasks. Our study shows the effective use
of RetainVis for gaining insights into how individual medical codes contribute
to making risk predictions, using EMRs of patients with heart failure and
cataract symptoms. Our study also demonstrates how we made substantial changes
to the state-of-the-art RNN model called RETAIN in order to make use of
temporal information and increase interactivity. This study will provide a
useful guideline for researchers that aim to design an interpretable and
interactive visual analytics tool for RNNs.Comment: Accepted at IEEE VIS 2018. To appear in IEEE Transactions on
Visualization and Computer Graphics in January 201
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
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data
whose complex temporal dynamics need to be studied. More especially, the event
timestamps can carry important information about the underlying network
dynamics, which otherwise are not available from the time-series evenly sampled
from continuous signals. Moreover, in most complex processes, event sequences
and evenly-sampled times series data can interact with each other, which
renders joint modeling of those two sources of data necessary. To tackle the
above problems, in this paper, we utilize the rich framework of (temporal)
point processes to model event data and timely update its intensity function by
the synergic twin Recurrent Neural Networks (RNNs). In the proposed
architecture, the intensity function is synergistically modulated by one RNN
with asynchronous events as input and another RNN with time series as input.
Furthermore, to enhance the interpretability of the model, the attention
mechanism for the neural point process is introduced. The whole model with
event type and timestamp prediction output layers can be trained end-to-end and
allows a black-box treatment for modeling the intensity. We substantiate the
superiority of our model in synthetic data and three real-world benchmark
datasets.Comment: 14 page
A Comparative Study for Predicting Heart Diseases Using Data Mining Classification Methods
Improving the precision of heart diseases detection has been investigated by
many researchers in the literature. Such improvement induced by the
overwhelming health care expenditures and erroneous diagnosis. As a result,
various methodologies have been proposed to analyze the disease factors aiming
to decrease the physicians practice variation and reduce medical costs and
errors. In this paper, our main motivation is to develop an effective
intelligent medical decision support system based on data mining techniques. In
this context, five data mining classifying algorithms, with large datasets,
have been utilized to assess and analyze the risk factors statistically related
to heart diseases in order to compare the performance of the implemented
classifiers (e.g., Na\"ive Bayes, Decision Tree, Discriminant, Random Forest,
and Support Vector Machine). To underscore the practical viability of our
approach, the selected classifiers have been implemented using MATLAB tool with
two datasets. Results of the conducted experiments showed that all
classification algorithms are predictive and can give relatively correct
answer. However, the decision tree outperforms other classifiers with an
accuracy rate of 99.0% followed by Random forest. That is the case because both
of them have relatively same mechanism but the Random forest can build ensemble
of decision tree. Although ensemble learning has been proved to produce
superior results, but in our case the decision tree has outperformed its
ensemble version
Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records
We present an interpretable neural network for predicting an important
clinical outcome (1-year mortality) from multi-modal Electronic Health Record
(EHR) data. Our approach builds on prior multi-modal machine learning models by
now enabling visualization of how individual factors contribute to the overall
outcome risk, assuming other factors remain constant, which was previously
impossible.
We demonstrate the value of this approach using a large multi-modal clinical
dataset including both EHR data and 31,278 echocardiographic videos of the
heart from 26,793 patients. We generated separate models for (i) clinical data
only (CD) (e.g. age, sex, diagnoses and laboratory values), (ii) numeric
variables derived from the videos, which we call echocardiography-derived
measures (EDM), and (iii) CD+EDM+raw videos (pixel data). The interpretable
multi-modal model maintained performance compared to non-interpretable models
(Random Forest, XGBoost), and also performed significantly better than a model
using a single modality (average AUC=0.82). Clinically relevant insights and
multi-modal variable importance rankings were also facilitated by the new
model, which have previously been impossible.Comment: Submitted to IEEE JBH
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.Comment: To appear in ICDM 2017. This is the full version of paper with 8
page
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction
Clinical outcome prediction based on the Electronic Health Record (EHR) plays
a crucial role in improving the quality of healthcare. Conventional deep
sequential models fail to capture the rich temporal patterns encoded in the
longand irregular clinical event sequences. We make the observation that
clinical events at a long time scale exhibit strongtemporal patterns, while
events within a short time period tend to be disordered co-occurrence. We thus
propose differentiated mechanisms to model clinical events at different time
scales. Our model learns hierarchical representationsof event sequences, to
adaptively distinguish between short-range and long-range events, and
accurately capture coretemporal dependencies. Experimental results on real
clinical data show that our model greatly improves over previous
state-of-the-art models, achieving AUC scores of 0.94 and 0.90 for predicting
death and ICU admission respectively, Our model also successfully identifies
important events for different clinical outcome prediction tasksComment: 10 pages, 2 figures, accepted by AMIA annual symposiu
- …