Machine Learning and Deep learning for Healthcare Data Processing and Analyzing
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 2191
Special Issue Editors
Interests: healthcare data; machine learning; deep learning; signal processing and image processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Healthcare data processing refers to the recording, storage, analysis and management of physiological data related to the healthcare industry. In the COVID-19 pandemic, AI-assisted diagnostics played an important role in the early detection of different pathologies and fine-grained classification of patients. The electronic medical records (EHRs) and AI algorithms are reshaping modern diagnostics, making precise medicine and data-driven healthcare in the big data era a reality. The healthcare data are recorded from the patients using biomedical signal recording instruments and medical imaging modalities, as well as wearable sensors. The automated analysis of healthcare data using AI algorithms is important for the diagnosis of various diseases. This Special Issue will help to demonstrate the applications of machine learning and deep learning for different healthcare data processing. This Special Issue welcomes high-quality original research papers and review papers on the applications of machine learning and deep learning methods for healthcare data analysis. We expect submissions of articles related but not limited to the following topics:
- Machine learning coupled with signal processing for electrocardiogram (ECG) data processing;
- Plethysmogram (PPG) data processing using machine learning coupled with signal processing;
- Electroencephalogram (EEG) data processing using signal processing and machine learning;
- Deep learning for EEG, ECG and PPG data processing;
- Machine learning and deep learning for medical image processing;
- Multimodal physiological data analysis using machine and deep learning techniques;
- Data-driven healthcare systems, meta-learning and multi-task learning for healthcare data analysis;
- Federated learning in healthcare data processing;
- Internet of Medical Things and Biomedical Embedded systems.
Dr. Rajesh K. Tripathy
Dr. Haipeng Liu
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. Diagnostics 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 2600 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
- machine learning
- deep learning
- artificial intelligence (AI)
- AI-assisted diagnostics
- multimodal clinical data
- data-driven healthcare
