Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study

Background Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. Objective As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. Methods In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. Results Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. Conclusions To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.

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Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study (Preprint)

10.2196/preprints.14502 ◽

2019 ◽

Author(s):

Po-Ting Lai ◽

Wei-Liang Lu ◽

Ting-Rung Kuo ◽

Chia-Ru Chung ◽

Jen-Chieh Han ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Network Architecture ◽

Disease Association ◽

Support Vector ◽

Context Aware ◽

Neural Network Architecture ◽

Large Margin

BACKGROUND Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. OBJECTIVE As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. METHODS In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. RESULTS Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.

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Automatically Extracting Disease-Disease Association from Literature with a Large Margin Context-Aware Convolutional Neural Network (Preprint)

10.2196/preprints.14129 ◽

2019 ◽

Author(s):

Po-Ting Lai ◽

Wei-Liang Lu ◽

Ting-Rung Kuo ◽

Chia-Ru Chung ◽

Jen-Chieh Han ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Disease Association ◽

Supervised Machine Learning ◽

Support Vector ◽

Context Aware ◽

Large Margin ◽

Machine Learning Classification ◽

Output Layer

BACKGROUND Research on disease-disease association, like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest disease association findings. For one thing, comorbidity and complication keywords pull up large numbers of PubMed studies. Secondly, disease is not highlighted in search results. Third, disease-disease association (DDA) is not identified, as currently no DDA extraction dataset or tools are available. OBJECTIVE Since there are no available disease-disease association extraction (DDAE) datasets or tools, we aim to develop (1) a DDAE dataset and (2) a neural network model for extracting DDAs from literature. METHODS In this study, we formulate DDAE as a supervised machine learning classification problem. To develop the system, we first build a DDAE dataset. We then employ two machine-learning models, support vector machine (SVM) and convolutional neural network (CNN), to extract DDAs. Furthermore, we evaluate the effect of using the output layer as features of the SVM-based model. Finally, we implement large margin context-aware convolutional neural network (LC-CNN) architecture to integrate context features and CNN through the large margin function. RESULTS Our DDAE dataset consists of 521 PubMed abstracts. Experiment results show that the SVM-based approach achieves an F1-measure of 80.32%, which is higher than the CNN-based approach (73.32%). Using the output layer of CNN as a feature for SVM does not further improve the performance of SVM. However, our LC-CNN achieves the highest F1-measure of 84.18%, and demonstrates combining the hinge loss function of SVM with CNN into a single NN architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we develop the first publicly available DDAE dataset consisting of disease mentions, MeSH IDs and relation annotations. We develop different conventional ML models and NN architectures, and evaluate their effects on our DDAE dataset. To further improve DDAE performance, we propose an LC-CNN model for DDAE that outperforms other approaches.

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HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽

10.3390/s21237853 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7853

Author(s):

Aleksej Logacjov ◽

Kerstin Bach ◽

Atle Kongsvold ◽

Hilde Bremseth Bårdstu ◽

Paul Jarle Mork

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Short Term Memory ◽

Human Activity Recognition ◽

Support Vector ◽

Free Living

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021824553 ◽

2021 ◽

Vol 8 (2) ◽

pp. 311

Author(s):

Mohammad Farid Naufal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Cross Validation ◽

Nearest Neighbors ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

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Object Detection Using Support Vector Machine and Convolutional Neural Network - A Survey

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.24.12128 ◽

2018 ◽

Vol 7 (2.24) ◽

pp. 428

Author(s):

Rishi Khosla ◽

Yashovardhan Singh ◽

T Balachander

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Mobile Device ◽

Confidence Score ◽

Mobile User ◽

Mobile Technologies ◽

Support Vector ◽

Scale Invariant

Mobile Technologies have been in trend for quite some time and with the advances in machine learning, they have become more powerful. Computer Vision, Computational Analysis and Computer Graphics have changed over the course of time. In this Project, our aim is to figure out the domains in which Machine Learning can be applied to enhance the capabilities of a Mobile Device which would lead to a better and sustainable mobile user experience. The models we would use are a convolutional neural network (CNN), support vector machine (SVM) and scale-invariant feature transform (SIFT). This project uses the real-time image from a mobile device and does the classification and detection with the help of Tensor Flow and provides the result with a confidence score.

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A Novel Multi Hidden Layer Convolutional Neural Network for Content Based Image Retrieval

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4771.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 365-370

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Network Architecture ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Content Based Image Retrieval ◽

Neural Network Architecture ◽

Query Image

The applications of a content-based image retrieval system in fields such as multimedia, security, medicine, and entertainment, have been implemented on a huge real-time database by using a convolutional neural network architecture. In general, thus far, content-based image retrieval systems have been implemented with machine learning algorithms. A machine learning algorithm is applicable to a limited database because of the few feature extraction hidden layers between the input and the output layers. The proposed convolutional neural network architecture was successfully implemented using 128 convolutional layers, pooling layers, rectifier linear unit (ReLu), and fully connected layers. A convolutional neural network architecture yields better results of its ability to extract features from an image. The Euclidean distance metric is used for calculating the similarity between the query image and the database images. It is implemented using the COREL database. The proposed system is successfully evaluated using precision, recall, and F-score. The performance of the proposed method is evaluated using the precision and recall.

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ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget

IoT ◽

10.3390/iot2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 222-235

Author(s):

Guillaume Coiffier ◽

Ghouthi Boukli Hacene ◽

Vincent Gripon

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

Network Architecture ◽

Deep Neural Networks ◽

State Of The Art ◽

Feature Maps ◽

Neural Network Architecture

Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations. This means that most of the parameters lay in the final layers, while a large portion of the computations are performed by a small fraction of the total parameters in the first layers. In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet. In ThriftyNet, only one convolutional layer is defined and used recursively, leading to a maximal parameter factorization. In complement, normalization, non-linearities, downsamplings and shortcut ensure sufficient expressivity of the model. ThriftyNet achieves competitive performance on a tiny parameters budget, exceeding 91% accuracy on CIFAR-10 with less than 40 k parameters in total, 74.3% on CIFAR-100 with less than 600 k parameters, and 67.1% On ImageNet ILSVRC 2012 with no more than 4.15 M parameters. However, the proposed method typically requires more computations than existing counterparts.

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Machine Learning Classification of Inflammatory Bowel Disease in Children Based on a Large Real-World Pediatric Cohort CEDATA-GPGE® Registry

Frontiers in Medicine ◽

10.3389/fmed.2021.666190 ◽

2021 ◽

Vol 8 ◽

Author(s):

Nicolas Schneider ◽

Keywan Sohrabi ◽

Henning Schneider ◽

Klaus-Peter Zimmer ◽

Patrick Fischer ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Support Vector ◽

Machine Learning Classification ◽

Extreme Gradient Boosting ◽

Inflammatory Bowel

Introduction: The rising incidence of pediatric inflammatory bowel diseases (PIBD) facilitates the need for new methods of improving diagnosis latency, quality of care and documentation. Machine learning models have shown to be applicable to classifying PIBD when using histological data or extensive serology. This study aims to evaluate the performance of algorithms based on promptly available data more suited to clinical applications.Methods: Data of inflammatory locations of the bowels from initial and follow-up visitations is extracted from the CEDATA-GPGE registry and two follow-up sets are split off containing only input from 2017 and 2018. Pre-processing excludes patients in remission and encodes the categorical data numerically. For classification of PIBD diagnosis, a support vector machine (SVM), a random forest algorithm (RF), extreme gradient boosting (XGBoost), a dense neural network (DNN) and a convolutional neural network (CNN) are employed. As best performer, a convolutional neural network is further improved using grid optimization.Results: The achieved accuracy of the optimized neural network reaches up to 90.57% on data inserted into the registry in 2018. Less performant methods reach 88.78% for the DNN down to 83.94% for the XGBoost. The accuracy of prediction for the 2018 follow-up dataset is higher than those for older datasets. Neural networks yield a higher standard deviation with 3.45 for the CNN compared to 0.83–0.86 of the support vector machine and ensemble methods.Discussion: The displayed accuracy of the convolutional neural network proofs the viability of machine learning classification in PIBD diagnostics using only timely available data.

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