A Manifest-Based Framework for Organizing the Management of Personal Data at the Edge of the Network

Smart disclosure initiatives and new regulations such as GDPR allow individuals to get the control back on their data by gathering their entire digital life in a Personal Data Management Systems (PDMS). Multiple PDMS architectures exist, from centralized web hosting solutions to self-data hosting at home. These solutions strongly differ on their ability to preserve data privacy and to perform collective computations crossing data of multiple individuals (e.g., epidemiological or social studies) but none of them satisfy both objectives. The emergence of Trusted Execution Environments (TEE) changes the game. We propose a solution called Trusted PDMS, combining the TEE and PDMS properties to manage the data of each individual, and a Manifest-based framework to securely execute collective computation on top of them. We demonstrate the practicality of the solution through a real case-study being conducted over 10.000 patients in the healthcare field

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management

A survey of OR/MS models on care planning for frail and elderly patients

Context With an ageing population, there is an increased demand on public health services and on long-term-care facilities. It is not uncommon for frail and elderly patients to spend longer in hospital or require more support within the community, often due to multi-morbidities. Many health services are faced with the complex problem as to how to administer the best care for the frail and elderly whilst best managing limited health resources. Objective This paper focuses on the literature concerning frail and elderly patient pathways within both hospital and community care settings with the use of Operations Research and Management Science (OR/MS) methods. To cover a wide range of specialities, the following additional subject areas have been included: Geriatrics and Gerontology, Health Policy and Services, Industrial Engineering, and Medical Informatics, to synthesise the work on modelling the application of care for frail and elderly patients. This review paper also analyses trends in the research literature and identifies gaps for future study. Methods A set of criteria has been established in which a systematic search was performed against to identify literature from 2000 to 2020. In total 62 publications were identified as applicable and were categorised methodologically and analysed. Common features of the papers including hospital setting, research aims and planning decisions have been identified and discussed. Results The results from the analysis reveal that this field of study is increasing with over 47% of papers having been published since 2015. The main findings suggest three areas of future research. Firstly, focus should be on modelling pathways holistically, with collaboration from both hospitals and community care settings. Secondly, work should be conducted on patient outcomes of these modelled pathways to highlight the increase in quality of care. Thirdly, there is potential for a wider variety of OR/MS methods to be utilised across the whole pathway. These three areas will reduce pressure on health services which are currently facing rising demands with limited resources

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging

COLAEVA: Visual Analytics and Data Mining Web-Based Tool for Virtual Coaching of Older Adult Populations

The global population is aging in an unprecedented manner and the challenges for improving the lives of older adults are currently both a strong priority in the political and healthcare arena. In this sense, preventive measures and telemedicine have the potential to play an important role in improving the number of healthy years older adults may experience and virtual coaching is a promising research area to support this process. This paper presents COLAEVA, an interactive web application for older adult population clustering and evolution analysis. Its objective is to support caregivers in the design, validation and refinement of coaching plans adapted to specific population groups. COLAEVA enables coaching caregivers to interactively group similar older adults based on preliminary assessment data, using AI features, and to evaluate the influence of coaching plans once the final assessment is carried out for a baseline comparison. To evaluate COLAEVA, a usability test was carried out with 9 test participants obtaining an average SUS score of 71.1. Moreover, COLAEVA is available online to use and explore.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 769830

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions