27,006 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
Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases
Automatic diagnosis of coronary heart disease helps the doctor to support in
decision making a diagnosis. Coronary heart disease have some types or levels.
Referring to the UCI Repository dataset, it divided into 4 types or levels that
are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models
can be analyzed with multiclass classification approach. One of multiclass
classification approach used, one of which is a support vector machine (SVM).
The SVM use due to strong performance of SVM in binary classification. This
research study multiclass performance classification support vector machine to
diagnose the type or level of coronary heart disease. Coronary heart disease
patient data taken from the UCI Repository. Stages in this study is
preprocessing, which consist of, to normalizing the data, divide the data into
data training and testing. The next stage of multiclass classification and
performance analysis. This study uses multiclass SVM algorithm, namely: Binary
Tree Support Vector Machine (BTSVM), One-Against-One (OAO), One-Against-All
(OAA), Decision Direct Acyclic Graph (DDAG) and Exhaustive Output Error
Correction Code (ECOC). Performance parameter used is recall, precision,
F-measure and Overall accuracy
CarePre: An Intelligent Clinical Decision Assistance System
Clinical decision support systems (CDSS) are widely used to assist with
medical decision making. However, CDSS typically require manually curated rules
and other data which are difficult to maintain and keep up-to-date. Recent
systems leverage advanced deep learning techniques and electronic health
records (EHR) to provide more timely and precise results. Many of these
techniques have been developed with a common focus on predicting upcoming
medical events. However, while the prediction results from these approaches are
promising, their value is limited by their lack of interpretability. To address
this challenge, we introduce CarePre, an intelligent clinical decision
assistance system. The system extends a state-of-the-art deep learning model to
predict upcoming diagnosis events for a focal patient based on his/her
historical medical records. The system includes an interactive framework
together with intuitive visualizations designed to support the diagnosis,
treatment outcome analysis, and the interpretation of the analysis results. We
demonstrate the effectiveness and usefulness of CarePre system by reporting
results from a quantities evaluation of the prediction algorithm and a case
study and three interviews with senior physicians
Fully Automated Myocardial Infarction Classification using Ordinary Differential Equations
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the
potential to be used as point-of-care for cardiovascular disease diagnostic
systems. Such wearable and wireless ECG systems require automatic detection of
cardiovascular disease. Even in the primary care, automation of ECG diagnostic
systems will improve efficiency of ECG diagnosis and reduce the minimal
training requirement of local healthcare workers. However, few fully automatic
myocardial infarction (MI) disease detection algorithms have well been
developed. This paper presents a novel automatic MI classification algorithm
using second order ordinary differential equation (ODE) with time varying
coefficients, which simultaneously captures morphological and dynamic feature
of highly correlated ECG signals. By effectively estimating the unobserved
state variables and the parameters of the second order ODE, the accuracy of the
classification was significantly improved. The estimated time varying
coefficients of the second order ODE were used as an input to the support
vector machine (SVM) for the MI classification. The proposed method was applied
to the PTB diagnostic ECG database within Physionet. The overall sensitivity,
specificity, and classification accuracy of 12 lead ECGs for MI binary
classifications were 98.7%, 96.4% and 98.3%, respectively. We also found that
even using one lead ECG signals, we can reach accuracy as high as 97%.
Multiclass MI classification is a challenging task but the developed ODE
approach for 12 lead ECGs coupled with multiclass SVM reached 96.4% accuracy
for classifying 5 subgroups of MI and healthy controls
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
Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis
The representation of knowledge based on first-order logic captures the
richness of natural language and supports multiple probabilistic inference
models. Although symbolic representation enables quantitative reasoning with
statistical probability, it is difficult to utilize with machine learning
models as they perform numerical operations. In contrast, knowledge embedding
(i.e., high-dimensional and continuous vectors) is a feasible approach to
complex reasoning that can not only retain the semantic information of
knowledge but also establish the quantifiable relationship among them. In this
paper, we propose recursive neural knowledge network (RNKN), which combines
medical knowledge based on first-order logic with recursive neural network for
multi-disease diagnosis. After RNKN is efficiently trained from manually
annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented
knowledge embeddings and weight matrixes are learned. Experimental results
verify that the diagnostic accuracy of RNKN is superior to that of some
classical machine learning models and Markov logic network (MLN). The results
also demonstrate that the more explicit the evidence extracted from CEMRs is,
the better is the performance achieved. RNKN gradually exhibits the
interpretation of knowledge embeddings as the number of training epochs
increases
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Objective: Electronic medical records (EMRs) contain an amount of medical
knowledge which can be used for clinical decision support (CDS). Our objective
is a general system that can extract and represent these knowledge contained in
EMRs to support three CDS tasks: test recommendation, initial diagnosis, and
treatment plan recommendation, with the given condition of one patient.
Methods: We extracted four kinds of medical entities from records and
constructed an EMR-based medical knowledge network (EMKN), in which nodes are
entities and edges reflect their co-occurrence in a single record. Three
bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each
task. One part of the bi-graph was the given condition (e.g., symptoms), and
the other was the condition to be inferred (e.g., diseases). Each bi-graph was
regarded as a Markov random field to support the inference. Three lazy energy
functions and one parameter-based energy function were proposed, as well as two
knowledge representation learning-based energy functions, which can provide a
distributed representation of medical entities. Three measures were utilized
for performance evaluation. Results: On the initial diagnosis task, 80.11% of
the test records identified at least one correct disease from top 10
candidates. Test and treatment recommendation results were 87.88% and 92.55%,
respectively. These results altogether indicate that the proposed system
outperformed the baseline methods. The distributed representation of medical
entities does reflect similarity relationships in regards to knowledge level.
Conclusion: Combining EMKN and MRF is an effective approach for general medical
knowledge representation and inference. Different tasks, however, require
designing their energy functions individually
Deep EHR: Chronic Disease Prediction Using Medical Notes
Early detection of preventable diseases is important for better disease
management, improved inter-ventions, and more efficient health-care resource
allocation. Various machine learning approacheshave been developed to utilize
information in Electronic Health Record (EHR) for this task. Majorityof
previous attempts, however, focus on structured fields and lose the vast amount
of information inthe unstructured notes. In this work we propose a general
multi-task framework for disease onsetprediction that combines both free-text
medical notes and structured information. We compareperformance of different
deep learning architectures including CNN, LSTM and hierarchical models.In
contrast to traditional text-based prediction models, our approach does not
require disease specificfeature engineering, and can handle negations and
numerical values that exist in the text. Ourresults on a cohort of about 1
million patients show that models using text outperform modelsusing just
structured data, and that models capable of using numerical values and
negations in thetext, in addition to the raw text, further improve performance.
Additionally, we compare differentvisualization methods for medical
professionals to interpret model predictions.Comment: Machine Learning for Health Care conferenc
Statistical feature embedding for heart sound classification
Cardiovascular Disease (CVD) is considered as one of the principal causes of
death in the world. Over recent years, this field of study has attracted
researchers' attention to investigate heart sounds' patterns for disease
diagnostics. In this study, an approach is proposed for normal/abnormal heart
sound classification on the Physionet challenge 2016 dataset. For the first
time, a fixed-length feature vector; called i-vector; is extracted from each
heart sound using Mel Frequency Cepstral Coefficient (MFCC) features.
Afterwards, Principal Component Analysis (PCA) transform and Variational
Autoencoder (VAE) are applied on the i-vector to achieve dimension reduction.
Eventually, the reduced size vector is fed to Gaussian Mixture Models (GMMs)
and Support Vector Machine (SVM) for classification purpose. Experimental
results demonstrate the proposed method could achieve a performance improvement
of 16% based on Modified Accuracy (MAcc) compared with the baseline system on
the Physoinet dataset
Health Analytics: a systematic review of approaches to detect phenotype cohorts using electronic health records
The paper presents a systematic review of state-of-the-art approaches to
identify patient cohorts using electronic health records. It gives a
comprehensive overview of the most commonly de-tected phenotypes and its
underlying data sets. Special attention is given to preprocessing of in-put
data and the different modeling approaches. The literature review confirms
natural language processing to be a promising approach for electronic
phenotyping. However, accessibility and lack of natural language process
standards for medical texts remain a challenge. Future research should develop
such standards and further investigate which machine learning approaches are
best suited to which type of medical data
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