scholarly journals Ontology-Guided Data Augmentation for Medical Document Classification

2020 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Gao Xiaoying ◽  
Mei Yi ◽  
Ghosh Shameek ◽  
Li Jinyan
Keyword(s):  
Input Data ◽  
Data Augmentation ◽  
Substantial Improvement ◽  
Document Classification ◽  
Suggested Approach ◽  
Domain Specific ◽  
Unstructured Text ◽  
Challenging Tasks ◽  
Medical Documents ◽  
Medical Document

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

scholarly journals Ontology-Guided Data Augmentation for Medical Document Classification

2020 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Gao Xiaoying ◽  
Mei Yi ◽  
Ghosh Shameek ◽  
Li Jinyan
Keyword(s):  
Input Data ◽  
Data Augmentation ◽  
Substantial Improvement ◽  
Document Classification ◽  
Suggested Approach ◽  
Domain Specific ◽  
Unstructured Text ◽  
Challenging Tasks ◽  
Medical Documents ◽  
Medical Document

Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

scholarly journals An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Classification Problem ◽  
Substantial Improvement ◽  
Two Stage ◽  
Data Set ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Second Stage ◽  
Medical Text ◽  
Medical Language ◽  
Medical Documents

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

scholarly journals Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Language Processing ◽  
Substantial Improvement ◽  
Document Classification ◽  
Clinical Notes ◽  
Unified Medical Language System ◽  
Second Stage ◽  
Third Stage ◽  
Medical Documents ◽  
Artery Disease

Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.

scholarly journals Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Language Processing ◽  
Substantial Improvement ◽  
Document Classification ◽  
Clinical Notes ◽  
Unified Medical Language System ◽  
Second Stage ◽  
Third Stage ◽  
Medical Documents ◽  
Artery Disease

Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.

scholarly journals An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

2021 ◽  
Author(s):  
Mahdi Abdollahi ◽  
Xiaoying Gao ◽  
Yi Mei ◽  
S Ghosh ◽  
J Li
Keyword(s):  
Classification Problem ◽  
Substantial Improvement ◽  
Two Stage ◽  
Data Set ◽  
Unified Medical Language System ◽  
Domain Specific ◽  
Second Stage ◽  
Medical Text ◽  
Medical Language ◽  
Medical Documents

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.

scholarly journals Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 7148
Author(s):  
Bedada Endale ◽  
Abera Tullu ◽  
Hayoung Shi ◽  
Beom-Soo Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Input Data ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Object Classification ◽  
Training Data ◽  
Gradient Descent Algorithm

Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.

scholarly journals Breast cancer detection using deep convolutional neural networks and support vector machines

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6201 ◽  
Author(s):  
Dina A. Ragab ◽  
Maha Sharkas ◽  
Stephen Marshall ◽  
Jinchang Ren
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Input Data ◽  
Data Augmentation ◽  
Area Under The Curve ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Convolutional Neural Networks ◽  
Cad System

It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.

scholarly journals Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

2020 ◽  
Vol 8 ◽  
pp. 141-155
Author(s):  
Kai Sun ◽  
Dian Yu ◽  
Dong Yu ◽  
Claire Cardie
Keyword(s):  
Reading Comprehension ◽  
Prior Knowledge ◽  
Data Augmentation ◽  
Multiple Choice ◽  
Model Performance ◽  
Free Form ◽  
World Knowledge ◽  
Domain Specific ◽  
Significant Performance ◽  
Machine Reading

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especiallyon problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C3 is available at https://dataset.org/c3/ .

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