Institute of Digital Health


Call for Papers: Machine Learning in Health and Biomedicine

The healthcare and biomedical research domains are currently inundated with digital information in a way that it has never been before: clinical and treatment outcomes data contained in electronic health records; physiological and biomedical data such as gene expression, epigenetics, proteomics and metabolomics; clinical imaging data and patient-oriented automated or point-of-care clinical data collection among many others. Over the last few decades, the field of machine learning has significantly transformed the data sciences by providing computational methodologies that induce knowledge or patterns from examples of data. Machine learning approaches are useful in cases where algorithmic solutions are not available, there is a lack of formal models, or the knowledge or concept about the application domain is poorly defined. Machine learning algorithms, techniques, frameworks and tools have proven to be suited (with different degrees of success) to solving diagnostic, treatment and prognostic problems in a variety of medical domains and their implementation can underpin computer-based systems that enhance the accuracy and efficiency of medical decision making. Equally important, these data intensive approaches are suitable to provide individual-centric insights (each patient presents a unique problem) of differences between patients that lead to personalised diagnosis, treatments and outcomes.

UCL Institute for Digital Health: “This timely PLoS cross-journal call for papers on ML for healthcare and biomedicine aims to amalgamate high-quality research on machine learning approaches and their use for improvement of human health globally”.

A team of Guest and Academic Editors for this Collection seeks research with direct clinical and health policy implications, studies that elucidate biological processes underlying health and disease, innovations in machine learning methodology and data provision, and other advances in the field.

Authors should specify the Call for Papers, “Machine Learning in Health and Biomedicine,” in their cover letter and, for PLOS ONE, in the ‘Collections’ field under ‘Additional Information.’ Manuscripts must be submitted in full; formal pre-submission inquiries will not be considered. Submit to PLOS Medicine; Submit to PLOS Computational Biology; Submit to PLOS ONE

Articles must be submitted by May 25, 2018. Research accepted for publication in PLOS Medicine will appear in a Special Issue to be published in late Fall 2018, along with commentary from leading experts in the field. The broader Collection, comprising all articles published in PLOS Computational Biology, PLOS ONE and PLOS Medicine, will launch in late Fall and continue into 2019.

Dr. Delmiro Fernandez-Reyes.

Department of Computer Science, University College London, UK.