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Deep Learning in Healthcare
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Deep Learning in Healthcare

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Health Informatics and Big Data".

Deadline for manuscript submissions: closed (1 November 2021) | Viewed by 5413

Special Issue Editors

Dr. Yimin Zhou

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: control theory and engineering; neural network and machine learning; robotics; image processing; fault diagnosis and tolerant control; smart grid; UAV; autonomous driving
Special Issues, Collections and Topics in MDPI journals
Dr. Zuguo Chen

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Xili University Town, Shenzhen 518055, China
Interests: big data processing; deep learning; pattern recognition; image processing; knowledge engineering and epidemic spreading behaviors analysis; etc.

Special Issue Information

Dear Colleagues,

This Special Issue focuses on data mining, big data analysis technology, deep convolution networks, generate against networks (GAN), gradient boosted machines (GBM) and deep reinforcement learning (DRL) to be applied in healthcare practices, such as disease transmission prediction, auxiliary diagnosis, post-recovery assessment of disease, new drug research and development, identification and early warning of psychological illness, health management, medical image recognition. The big data technology can be applied to the analysis and mining of heterogeneous data, and the analysis of a large number of real-time monitoring data, etc., which can provide technical support for healthcare management systems and comprehensive information platforms. In addition, big data technology can provide: clinical decision-making assistance and scientific research support for doctors; management assistance for the administration; health monitoring support for hospitals and residents; statistical analysis and medical behavior analysis support for new drug research and development.

Dr. Yimin Zhou
Dr. Zuguo Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data fusion
  • machine learning
  • health management
  • neural network modelling and prediction
  • disease propagation prediction
  • disease assistance diagnosis

Published Papers (2 papers)

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Research

13 pages, 8650 KiB  
Article
Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables
by Wenjia Chen and Jinlin Li
Healthcare 2021, 9(8), 992; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080992 - 04 Aug 2021
Cited by 5 | Viewed by 1613
Abstract
To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. The proposed ensemble CNN attention-based BILSTM model (ECA-BILSTM) combines shallow convolutional neural networks (CNNs), attention mechanisms, and bidirectional long short-term memory (BILSTM). Moreover, additional variables [...] Read more.
To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. The proposed ensemble CNN attention-based BILSTM model (ECA-BILSTM) combines shallow convolutional neural networks (CNNs), attention mechanisms, and bidirectional long short-term memory (BILSTM). Moreover, additional variables are selected according to the characteristics of teleconsultation demand and added to the inputs of forecasting models. To verify the superiority of ECA-BILSTM and the effectiveness of additional variables, two actual teleconsultation datasets collected in the National Telemedicine Center of China (NTCC) are used as the experimental data. Results showed that ECA-BILSTMs can significantly outperform corresponding benchmark models. And two key additional variables were identified for teleconsultation demand prediction improvement. Overall, the proposed ECA-BILSTM model with effective additional variables is a feasible promising approach in teleconsultation demand forecasting. Full article
(This article belongs to the Special Issue Deep Learning in Healthcare)
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19 pages, 4753 KiB  
Article
Multimodal Early Alzheimer’s Detection, a Genetic Algorithm Approach with Support Vector Machines
by Ana G. Sánchez-Reyna, José M. Celaya-Padilla, Carlos E. Galván-Tejada, Huizilopoztli Luna-García, Hamurabi Gamboa-Rosales, Andres Ramirez-Morales, Jorge I. Galván-Tejada and on behalf of the Alzheimer’s Disease Neuroimaging Initiative
Healthcare 2021, 9(8), 971; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare9080971 - 31 Jul 2021
Cited by 7 | Viewed by 2307
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive impairment (MCI) (prodromal stage [...] Read more.
Alzheimer’s disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive impairment (MCI) (prodromal stage of AD), is essential for early care of the disease. As a result, machine learning techniques have been used in recent years for the diagnosis of AD. In this research, we propose a novel methodology to generate a multivariate model that combines different types of features for the detection of AD. In order to obtain a robust biomarker, ADNI baseline data, clinical and neuropsychological assessments (1024 features) of 106 patients were used. The data were normalized, and a genetic algorithm was implemented for the selection of the most significant features. Subsequently, for the development and validation of the multivariate classification model, a support vector machine model was created, and a five-fold cross-validation with an AUC of 87.63% was used to measure model performance. Lastly, an independent blind test of our final model, using 20 patients not considered during the model construction, yielded an AUC of 100%. Full article
(This article belongs to the Special Issue Deep Learning in Healthcare)
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