Medical Knowledge-Based Deep Learning Framework for Disease Prediction on Unstructured Radiology Free-Text Reports Under Low Data Condition

2020 ◽  
pp. 352-364
Author(s):  
Shashank Shetty ◽  
V. S. Ananthanarayana ◽  
Ajit Mahale
Keyword(s):  
Deep Learning ◽  
Medical Knowledge ◽  
Free Text ◽  
Disease Prediction ◽  
Learning Framework ◽  
Knowledge Based

scholarly journals Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jing Xu ◽  
Liang Gan ◽  
Mian Cheng ◽  
Quanyuan Wu
Keyword(s):  
Decision Support ◽  
Medical Knowledge ◽  
Great Difficulty ◽  
Entity Recognition ◽  
Free Text ◽  
Data Annotation ◽  
Medical Text ◽  
Knowledge Based ◽  
Medical Entity ◽  
Unstructured Information

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.

A Novel Disease Diagnosis Method Using Combining Knowledge Graph and Deep Learning

2021 ◽  
Vol 11 (7) ◽  
pp. 1845-1851
Author(s):  
Xi Cai
Keyword(s):  
Deep Learning ◽  
Medical Knowledge ◽  
Disease Diagnosis ◽  
Knowledge Map ◽  
Data Sets ◽  
Text Data ◽  
Knowledge Based ◽  
Channel Input ◽  
Convolution Process ◽  
Diagnosis Method

Disease diagnosis methods based on deep learning have some shortcomings in the auxiliary diagnosis process, such as relying heavily on labeled data and lack of doctor or expert experience knowledge. Based on the above background, this study proposes a disease diagnosis method combining medical knowledge atlas and deep learning (CKGDL). The core of the method is a knowledge-driven convolutional neural network (CNN) model. The structured disease knowledge in the medical knowledge map is obtained through entity link disambiguation and knowledge map embedding and extraction. The disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of CNN, and different types of diseases are expressed from the semantic and knowledge levels in the convolution process. Through training and testing on multiple types of disease description text data sets, the experimental results show that the diagnostic performance of this method is better than that of a single CNN model and other disease diagnosis methods. And further verified that this method of joint training of knowledge and data is more suitable for the initial diagnosis of the disease.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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