Predictive Modelling in Healthcare
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: closed (31 August 2023) | Viewed by 6726
Special Issue Editor
2. Institute of Molecular Biology and Biotechnology, Department of Biomedical Research—FORTH, University Campus of Ioannina, GR 45110 Ioannina, Greece
Interests: computational modelling of physiological systems; machine learning and pattern recognition-based analysis of biomedical data; AI-based healthcare recommendation systems
Special Issue Information
Dear Colleagues,
One individual’s biology, behaviour, and context can nowadays be precisely captured into new prospective data generated in the context of ad hoc cross-sectional or longitudinal clinical studies. Research questions defined upon a specific dataset and pertaining to the prediction, either diagnosis or prognosis, of health outcomes are formulated as machine learning (ML; inclusive of classical and deep learning) regression or classification multivariate functions of biomedical data. ML and linear system identification are essential for predicting time series data alike. Importantly, the advent of new guidelines contributes not only to enhancing the transparency of reporting of the conducted analyses but also to correcting the bias in the data-selection process and across the entire ML pipeline. In this context, the aim of this Special Issue is to present prediction model studies of health outcomes, tapping into systematic data quality assessment methods, novel ML architectures, robust cross-validation or external validation procedures and proper correctness and model relevance metrics. Special attention shall be placed on studies leveraging multimodal ML, multiscale modelling, adaptive learning of dynamic systems, and sparse ML. Whist high predictive performance is the mandate in predictive modelling in healthcare, additional quality properties, e.g., expressing interpretability, adversarial robustness, and fairness, are of paramount importance as well. In this direction, studies focusing on techniques for testing ML systems, as they are applied in predictive modelling paradigms in healthcare, shall be featured here.
Dr. Eleni Georga
Guest Editor
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.
