Diagnosis and Analysis of Diabetic Retinopathy Based on Electronic Health Records

Sun, Yunlei; Zhang, Dalin

doi:10.1109/access.2019.2918625

Cited by 43 publications

(20 citation statements)

References 18 publications

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“…In [78], five machine learning methods are employed to diagnose diabetic retinopathy (DR) from EHR data. A large retinal dataset comprised of 5057 records is collected from 301 hospitals in China.…”

Section: ) Decision Tree Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Section: ) Decision Tree Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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“…There are several approaches for the automatic detection of MAs in color retinal images. These approaches can be generally classified into three groups which use morphologicalbased approaches, template matching, or supervised learning or some combination of each [9], [11], [22]- [36]. Similarly, some of these works aim for the automatic detection of DR [27], [28], [35], [36].…”

Section: Previous Workmentioning

confidence: 99%

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

et al. 2021

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Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR). Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR. However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions. Recently, the emergence of computer-aided diagnosis systems offers promising approaches to detect these lesions for diagnostic purposes. In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem. In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection. In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows. Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier. The method was tested on three publicly available datasets and a local database comprising a total of 749 images. Detection performance was evaluated using sensitivity, specificity, and FROC analysis. From the image analysis viewpoint, DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 95.7% with an average of 7 false positives per image. These results compare favourably with the best of the published results to date.

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“…Diabetic complications can be caused by long-term uncontrolled blood glucose and one of the most common complications is diabetic retinopathy [51]. In order to carry out its early diagnosis, Arunkumar proposed a DBN method to extract features by extracting variables related to diabetic retinopathy, which also contributes to the development of automated screening systems [52].…”

Section: Deep Belief Networkmentioning

confidence: 99%

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Sun

Zhang

2019

Teh. vjesn.

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Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.

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Diagnosis and Analysis of Diabetic Retinopathy Based on Electronic Health Records

Cited by 43 publications

References 18 publications

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

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