Deep Learning for Healthcare Applications and Analysis
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4816
Special Issue Editors
Interests: business analytics & data mining; deep leaning; artificial intelligence applications; big data research; reliability prediction; six-sigma; quality & reliability engineering
Special Issue Information
Dear Colleagues,
Deep learning (DL) helps to construct efficient methods to learn the low-level structure of original data to obtain more intellectual descriptions, implicitly capturing complicated relations and features of large-scale input. In recent years, both theory development and practical applications of deep learning have significantly renovated healthcare areas. Due to the large-scale medical data that currently exist and the vast potential of deep learning, this field's state of the art has become a high-level research hot spot, outperforming conventional methods.
Despite the promising results obtained using deep learning, several unsolved challenges remain in relation to the clinical application of deep learning to healthcare. Some critical issues involved are data volume, quality, temporality, domain complexity, and interpretability.
These challenges introduce numerous chances and opportunities for future research to advance the field. Therefore, with this in mind, some of the identified promising future directions of deep learning in healthcare are: feature enrichment, federated inference, model privacy, incorporating expert knowledge, temporal modeling, and interpretable modeling. These future directions are considered in this Special Issue, with possible significant solutions.
Manuscripts, including high-quality research articles and critical reviews, are welcome for submission to this Special Issue on “Deep Learning for Healthcare Applications and Analysis”.
Topics of interest for this Special Issue include, but are not limited to:
- Medical imaging and diagnostics;
- Electronic health records analysis;
- Genomics data analysis;
- Drug discovery;
- Simplifying clinical trials;
- Personalized treatment;
- Improved health records and patient monitoring;
- Health insurance and fraud detection;
- Temporal healthcare data analysis.
Dr. Bharatendra Rai
Dr. S.A. Senthil Kumar
Guest Editors
Manuscript Submission Information
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