Deep Learning Methods for the Prediction of Chronic Diseases: A Systematic Review

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.103801 ◽

2020 ◽

Vol 122 ◽

pp. 103801 ◽

Cited By ~ 9

Author(s):

Shenda Hong ◽

Yuxi Zhou ◽

Junyuan Shang ◽

Cao Xiao ◽

Jimeng Sun

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Learning Methods

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Deep Learning-Based Models for Pain Recognition: A Systematic Review

10.20944/preprints202008.0040.v1 ◽

2020 ◽

Author(s):

Rasha M. Al-Eidan ◽

Hend Al-Khalifa ◽

AbdulMalik Alsalman

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Data Sets ◽

Observer Variability ◽

Learning Models ◽

Learning Methods ◽

Inter Observer Variability ◽

Recognition Systems ◽

Pain Recognition ◽

Open Issues

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

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Recommendation system based on deep learning methods: a systematic review and new directions

Artificial Intelligence Review ◽

10.1007/s10462-019-09744-1 ◽

2019 ◽

Vol 53 (4) ◽

pp. 2709-2748 ◽

Cited By ~ 5

Author(s):

Aminu Da’u ◽

Naomie Salim

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Recommendation System ◽

Learning Methods ◽

New Directions

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Deep-Learning-Based Models for Pain Recognition: A Systematic Review

Applied Sciences ◽

10.3390/app10175984 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5984

Author(s):

Rasha M. Al-Eidan ◽

Hend Al-Khalifa ◽

AbdulMalik Al-Salman

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Data Sets ◽

Observer Variability ◽

Learning Models ◽

Learning Methods ◽

Inter Observer Variability ◽

Recognition Systems ◽

Pain Recognition ◽

Open Issues

Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues.

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Deep Learning in mHealth for Chronic Diseases: A Systematic Review (Preprint)

10.2196/preprints.32344 ◽

2021 ◽

Author(s):

Andreas Triantafyllidis ◽

Haridimos Kondylakis ◽

Dimitrios Katehakis ◽

Angelina Kouroubali ◽

Lefteris Koumakis ◽

...

Keyword(s):

Systematic Review ◽

Neural Networks ◽

Cardiovascular Disease ◽

Deep Learning ◽

Chronic Diseases ◽

Real Life ◽

Bibliographic Databases ◽

Target Disease ◽

Using Data ◽

Almost All

BACKGROUND Major chronic diseases such as cardiovascular disease, diabetes, and cancer impose a significant burden on people and the healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential towards the development of intelligent mobile health (mHealth) interventions for chronic diseases which could revolutionize the delivery of healthcare anytime-anywhere. OBJECTIVE To present a systematic review of studies which have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases, and advance our understanding of the progress made in this rapidly developing field. METHODS We searched the bibliographic databases of Scopus and PubMed in order to identify papers with focus on the employment of DL algorithms using data captured from mobile devices (e.g., smartphones, smartwatches, and other wearable devices), and targeting cardiovascular disease, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, the study period, as well as the employed DL algorithm, the main DL outcome, the dataset used, the features selected, and the achieved performance. RESULTS 20 studies were included in the review. 7 DL studies (35%) targeted cardiovascular disease, 9 studies (45%) targeted diabetes, and 4 studies (20%) targeted cancer. The most common DL outcome was diagnosis of patient condition for the cardiovascular disease studies, prediction of blood glucose values for studies in diabetes, and early detection of cancer. The DL algorithms employed most were convolutional neural networks and recurrent neural networks. The performance of DL was found overall to be satisfactory reaching more than 84% accuracy in the majority of the studies. Almost all studies did not provide details on the explainability of DL outcomes. CONCLUSIONS The use of DL can facilitate the diagnosis, management and treatment of major chronic diseases through harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth interventions.

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