Digital Health in Focus of Predictive, Preventive and Personalised Medicine

Abstract

Together, modern innovations in the interconnected fields of precision medicine, machine learning, bioinformatics, information technology, and artificial intelligence offer an array of applications for tailoring the delivery of quality patient-centered care, advancing biomedical research, and streamlining the routine operations of entire health systems. In Digital Health in Focus of Predictive, Preventive and Personalised Medicine, Lotfi Chaari presents a collection of 18 original research contributions that were presented at the second annual International Conference on Digital Health Technologies (ICDHT 2019). For readers actively engaged in these technological domains along with those hoping to gain early insight, these articles highlight the contemporary paradigm shift in which big data meets preventive medicine. This quantitative-driven volume of the full book series, Advances in Predictive, Preventive and Personalised Medicine by Olga Golubnitschaja, explores recent advances in digital health applications designed to effectively collect, process, and analyze large sums of data as health systems around the world are faced with an ever-growing body of information relevant to patients’ health status and well-being. Based on current challenges, one of the included studies, for instance, employed machine learning methods to identify relevant medical documents and then visualize relationships between extrapolated themes. Another study summarized the role of big data in healthcare with a particular focus on the information collected and stored on wearable devices and their utility in promoting health, preventing disease, and enabling proactive conversations between patients and their physicians. Alongside the rise in patient-level data accessible for the development of personalized diagnostic strategies and therapeutic options, is the transition away from a one-size-fits-all approach to addressing the growing burden of chronic diseases across aging populations. Accordingly, Chaari includes articles specifically examining deep learning approaches to predict lung cancer risk, digital modelling methods to diagnose heart disease, and strategies for supporting older individuals affected by Alzheimer’s disease while also alleviating the burden placed on their caregivers. Studies addressing the latter ranged from the development of a more inclusive, multilingual memory-aid for elderly adults experiencing memory loss to the conceptualization of a system of home-based sensors that continuously monitor adults’ level of activity and ultimately guide whether professional assistance and caregiving are necessary. As a collection of studies describing innovative approaches to integrating digital health technologies into the provision of precision medicine, these articles implicitly underscore the contemporary and future burden placed on global health systems along with the seemingly infinite volume of data generated through biomedical imaging scans, electronic health records, and other diagnostic and screening modalities. Future work in this space may benefit further from a more cohesive grouping of rigorous, evidence-based research that captures the practical applications of technology in precision medicine. Furthermore, this selection of studies – when combined with external evidence highlighting data privacy, security, ethics, and clinical considerations – offers readers a thought-provoking glimpse into how innovations in technology foster advances in health.