Deep Learning and Data Balancing Approaches in Mining Hospital Surveillance Data

A number of classifier models on hospital surveillance data to classify admitted patients according to their critical conditions with an emphasis to deep learning paradigms, namely convolutional neural network, were used in this research. Three class labels were used to distinguish the criticality of the admitted 25,261 patients. The authors have set forth two distinct approaches to address the unbalance nature of data. They used multilayer perceptron (MLP), convolutional neural network (CNN), and multinomial logistic regression classifications and finally compared the performance of our models with the models developed by Firoze, Hasan and Rahman (2013). After comparison, the authors show that one of the models, including convolutional neural network based on deep learning, surpasses most models in terms of classification performance in contingent with training times and epochs. The trade-off is computational power for which—to achieve optimal accuracy—multiple CUDA cores are necessary. The authors achieved stable improvement of classification for their model using CNN.

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Mining ICDDR, B Hospital Surveillance Data and Exhibiting Strategies for Balancing Large Unbalanced Datasets

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2015010103 ◽

2015 ◽

Vol 10 (1) ◽

pp. 39-66 ◽

Cited By ~ 1

Author(s):

Adnan Firoze ◽

Rashedur M. Rahman

Keyword(s):

Large Scale ◽

Multinomial Logistic Regression ◽

Short Interval ◽

Classification Performance ◽

Computer Applications ◽

Surveillance Data ◽

Critical Conditions ◽

Locally Linear Embedding ◽

Support Vector ◽

Fitting Problem

This research uses a number of classifier models on Hospital Surveillance data to classify admitted patients according to their critical conditions. Three class labels were used to distinguish the criticality of the admitted patients. Furthermore, set forth are two distinct approaches to address the over-fitting problem in the unbalanced dataset since the frequency of instances of the class ‘low' is significantly higher than the other two classes. Apart from trimming the dataset to balance the classes, this work has dealt with the over-fitting problem by introducing the ‘Synthetic Minority Over-sampling Technique' (SMOTE) algorithm coupled with Locally Linear Embedding (LLE). It has constructed three models that applied the neural, and multinomial logistic regression classifications and finally compared the performance of the work's models with the models developed by Rahman and Hasan (2011) where they used several decision tree models to classify the same dataset using tenfold cross validation. Additionally, for a comprehensive comparative analysis, this work has compared the classification performance of the authors' novel third model using support vector machine (SVM). After comparison, the work shows that one of the authors' models surpasses all prior models in terms of classification performance, taking into account the performance time trade-off, giving them an efficient model that handles large scale unbalanced datasets efficiently with standard classification performance. The models developed in this research can become imperative tools to doctors when large numbers of patients arrive in a short interval especially during epidemics. Since, intervention of machines become a necessity when doctors are scarce, computer applications powered by these models are helpful to diagnose and measure the criticality of the newly arrived patients with the help of the historical data kept in the surveillance database.

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An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Journal of Healthcare Engineering ◽

10.1155/2019/4180949 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 44

Author(s):

Okeke Stephen ◽

Mangal Sain ◽

Uchenna Joseph Maduh ◽

Do-Un Jeong

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Data Augmentation ◽

Classification Performance ◽

Learning Approaches ◽

X Ray ◽

Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

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A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

10.5753/eniac.2018.4406 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jessica Barbosa Diniz ◽

Filipe R. Cordeiro ◽

Pericles B. C. Miranda ◽

Laura A. Tomaz Da Silva

Keyword(s):

Neural Network ◽

Deep Learning ◽

Genetic Programming ◽

Convolutional Neural Network ◽

Research Area ◽

Classification Performance ◽

Programming Approach ◽

Network Architectures ◽

Neural Network Architectures ◽

Grammar Based Genetic Programming

Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.

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Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834459 ◽

2021 ◽

Vol 8 (3) ◽

pp. 619

Author(s):

Candra Dewi ◽

Andri Santoso ◽

Indriati Indriati ◽

Nadia Artha Dewi ◽

Yoke Kusuma Arbawa

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Color Space ◽

Training Data ◽

Color Spaces ◽

Deep Learning Algorithm ◽

Optimal Accuracy

Semakin meningkatnya jumlah penderita diabetes menjadi salah satu faktor penyebab semakin tingginya penderita penyakit diabetic retinophaty. Salah satu citra yang digunakan oleh dokter mata untuk mengidentifikasi diabetic retinophaty adalah foto retina. Dalam penelitian ini dilakukan pengenalan penyakit diabetic retinophaty secara otomatis menggunakan citra fundus retina dan algoritme Convolutional Neural Network (CNN) yang merupakan variasi dari algoritme Deep Learning. Kendala yang ditemukan dalam proses pengenalan adalah warna retina yang cenderung merah kekuningan sehingga ruang warna RGB tidak menghasilkan akurasi yang optimal. Oleh karena itu, dalam penelitian ini dilakukan pengujian pada berbagai ruang warna untuk mendapatkan hasil yang lebih baik. Dari hasil uji coba menggunakan 1000 data pada ruang warna RGB, HSI, YUV dan L*a*b* memberikan hasil yang kurang optimal pada data seimbang dimana akurasi terbaik masih dibawah 50%. Namun pada data tidak seimbang menghasilkan akurasi yang cukup tinggi yaitu 83,53% pada ruang warna YUV dengan pengujian pada data latih dan akurasi 74,40% dengan data uji pada semua ruang warna. AbstractIncreasing the number of people with diabetes is one of the factors causing the high number of people with diabetic retinopathy. One of the images used by ophthalmologists to identify diabetic retinopathy is a retinal photo. In this research, the identification of diabetic retinopathy is done automatically using retinal fundus images and the Convolutional Neural Network (CNN) algorithm, which is a variation of the Deep Learning algorithm. The obstacle found in the recognition process is the color of the retina which tends to be yellowish red so that the RGB color space does not produce optimal accuracy. Therefore, in this research, various color spaces were tested to get better results. From the results of trials using 1000 images data in the color space of RGB, HSI, YUV and L * a * b * give suboptimal results on balanced data where the best accuracy is still below 50%. However, the unbalanced data gives a fairly high accuracy of 83.53% with training data on the YUV color space and 74,40% with testing data on all color spaces.

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

10.20944/preprints201809.0481.v1 ◽

2018 ◽

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g. hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause for the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: 1) a long short-term memory (LSTM); 2) a proposed spectrogram-based convolutional neural network model (pCNN); and 3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (manual) feature engineering. Results were evaluated on our own, publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

JMIR Medical Informatics ◽

10.2196/24973 ◽

2021 ◽

Vol 9 (1) ◽

pp. e24973

Author(s):

Thao Thi Ho ◽

Jongmin Park ◽

Taewoo Kim ◽

Byunggeon Park ◽

Jaehee Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

High Risk ◽

Convolutional Neural Network ◽

Clinical Data ◽

Area Under The Curve ◽

Classification Performance ◽

Imaging Data ◽

Learning Models ◽

Predictive Tool

Background Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.

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A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning

Applied Sciences ◽

10.3390/app10051605 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1605 ◽

Cited By ~ 4

Author(s):

Feng Li ◽

Fan He ◽

Fei Wang ◽

Dengyong Zhang ◽

Yi Xia ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Recognition Rate ◽

Classification Performance ◽

Eeg Signals ◽

Time Frequency ◽

Right Hand ◽

Left And Right

Left and right hand motor imagery electroencephalogram (MI-EEG) signals are widely used in brain-computer interface (BCI) systems to identify a participant intent in controlling external devices. However, due to a series of reasons, including low signal-to-noise ratios, there are great challenges for efficient motor imagery classification. The recognition of left and right hand MI-EEG signals is vital for the application of BCI systems. Recently, the method of deep learning has been successfully applied in pattern recognition and other fields. However, there are few effective deep learning algorithms applied to BCI systems, particularly for MI based BCI. In this paper, we propose an algorithm that combines continuous wavelet transform (CWT) and a simplified convolutional neural network (SCNN) to improve the recognition rate of MI-EEG signals. Using the CWT, the MI-EEG signals are mapped to time-frequency image signals. Then the image signals are input into the SCNN to extract the features and classify them. Tested by the BCI Competition IV Dataset 2b, the experimental results show that the average classification accuracy of the nine subjects is 83.2%, and the mean kappa value is 0.651, which is 11.9% higher than that of the champion in the BCI Competition IV. Compared with other algorithms, the proposed CWT-SCNN algorithm has a better classification performance and a shorter training time. Therefore, this algorithm could enhance the classification performance of MI based BCI and be applied in real-time BCI systems for use by disabled people.

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Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification

Remote Sensing ◽

10.3390/rs12101694 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1694 ◽

Cited By ~ 1

Author(s):

Yuwei Guo ◽

Zhuangzhuang Sun ◽

Rong Qu ◽

Licheng Jiao ◽

Fang Liu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Computational Complexity ◽

Convolutional Neural Network ◽

Image Classification ◽

Classification Performance ◽

Unlabeled Data ◽

Training Set ◽

Supervised Methods ◽

High Computational Complexity

Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels.

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Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

10.26434/chemrxiv.8150666.v1 ◽

2019 ◽

Author(s):

Seoin Back ◽

Junwoong Yoon ◽

Nianhan Tian ◽

Wen Zhong ◽

Kevin Tran ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

High Throughput ◽

High Throughput Screening ◽

Binding Energies ◽

Surface Structures ◽

Voronoi Polyhedra ◽

Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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