Machine Learning Approaches for Biomedical Prediction
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 20 November 2024 | Viewed by 139
Special Issue Editors
Interests: wearable sensors; machine learning; personalized medicine; health technology assessment
Interests: precision medicine; data mining; machine learning; big data; IoT; semantic web; linked data visualization; NLP
Special Issues, Collections and Topics in MDPI journals
Interests: brain; cellular biophysics; drug delivery systems; lipid bilayers; nanomedicine; neurophysiology; bioelectric phenomena; biological effects of fields; biomedical MRI; biomedical materials; biomembranes; diseases; drugs; learning (artificial intelligence); medical disorders; medical image processing; molecular biophysics; patient diagnosis; patient monitoring; smart phones; transcranial magnetic stimulation; audio signal processing; biodiffusion; biological tissues; biomagnetism
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning techniques for time series analysis and forecasting; distributed learning algorithms; neural and fuzzy neural models for ICT and industrial applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue titled "Machine Learning Approaches for Biomedical Prediction" aims to delve into the cutting-edge applications of machine learning within the realm of predicting and comprehending different biomedical phenomena. We invite scholarly contributions that span a broad spectrum of topics, ranging from the intricate nuances of predictive modeling for disease diagnosis, prognosis, and treatment response to the sophisticated integration of multi-modal biomedical data. Of particular significance is the emphasis on the transformative role played by innovative machine learning methodologies in propelling forward the domains of healthcare analytics, precision medicine, and telemedicine.
Furthermore, we extend an invitation to authors to present submissions that explore the intersection between machine learning and space exploration. This intriguing dimension seeks to stimulate insightful discussions on how these advanced technologies can significantly contribute to our understanding of health within the unique and challenging environment of space.
Dr. Antonio Pallotti
Dr. Noemi Scarpato
Dr. Alessandra Paffi
Dr. Antonello Rosato
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. Applied Sciences 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 2400 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
- biomedical prediction
- predictive modeling
- disease diagnosis
- treatment response
- biomedical data analysis
- feature selection
- precision medicine
- healthcare analytics
- computational biology
- telemedicine
- space exploration
