Overview of Network Analysis in Systems Medicine

Systems Medicine (SM) is an interdisciplinary research paradigm, that heavily relieson complex systems theory, and emphasizes on the studies the human body in termsof systems and the interactions among them, incorporating biochemical,physiological, and environment interactions. The article presents developments in SMresearch, focusing specifically on the network analysis approaches. Network analysisis fundamental for the study of interactions among systems at different levels withinthe human body. The background knowledge is established: the basic concepts ofnodes and edges, and network metrics as well as existing computational tools aredescribed. Different applications in health research are discussed, includingdescriptive and predictive approaches. The use of network analysis in temporal dataand data coming from digital health technologies is further highlighted. Finally, thecurrent challenges are discussed and the foreseen development

Towards Explainable and Trustworthy AI for Decision Support in Medicine: An Overview of Methods and Good Practices

Artificial Intelligence (AI) is defined as intelligence exhibited by machines, such as electronic computers. It can involve reasoning, problem solving, learning and knowledge representation, which are mostly in focus in the medical domain. Other forms of intelligence, including autonomous behavior, are also parts of AI. Data driven methods for decision support have been employed in the medical domain for some time. Machine learning (ML) is used for a wide range of complex tasks across many sectors of the industry. However, a broader spectrum of AI, including deep learning (DL) as well as autonomous agents, have been recently gaining more focus and have risen expectation for solving numerous problems in the medical domain. A barrier towards AI adoption, or rather a concern, is trust in AI, which is often hindered by issues like lack of understanding of a black-box model function, or lack of credibility related to reporting of results. Explainability and interpretability are prerequisites for the development of AI-based systems that are lawful, ethical and robust. In this respect, this paper presents an overview of concepts, best practices, and success stories, and opens the discussion for multidisciplinary work towards establishing trustworthy AI

PATHway: decision support in exercise programmes for cardiac rehabilitation

Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach

Enhanced Deep Learning Methodologies and MRI Selection Techniques for Dementia Diagnosis in the Elderly Population

Dementia, a debilitating neurological condition affecting millions worldwide, presents significant diagnostic challenges. In this work, we introduce a novel methodology for the classification of demented and non-demented elderly patients using 3D brain Magnetic Resonance Imaging (MRI) scans. Our approach features a unique technique for selectively processing MRI slices, focusing on the most relevant brain regions and excluding less informative sections. This methodology is complemented by a confidence-based classification committee composed of three custom deep learning models: Dem3D ResNet, Dem3D CNN, and Dem3D EfficientNet. These models work synergistically to enhance decision-making accuracy, leveraging their collective strengths. Tested on the Open Access Series of Imaging Studies(OASIS) dataset, our method achieved an impressive accuracy of 94.12%, surpassing existing methodologies. Furthermore, validation on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset confirmed the robustness and generalizability of our approach. The use of explainable AI (XAI) techniques and comprehensive ablation studies further substantiate the effectiveness of our techniques, providing insights into the decision-making process and the importance of our methodology. This research offers a significant advancement in dementia diagnosis, providing a highly accurate and efficient tool for clinical applications

Vectors and drivers of connected health in Europe: a foundation for integrated care

Coordinated, integrated care requires connected “inputs, delivery, management and organization of services related to diagnosis, treatment, care, rehabilitation and health promotion” (Grone & Barbero, 2002). Connected health (CH) offers a key building block as a “paradigm shift, looking after the individual and community health in a process that speaks to the health journey of the person, through the entire lifespan, leveraging a variety of technologies to do so” (ENJECT, 2016). However, CH is failing to reach its full potential – and therefore failing in its contribution to the realization of integrated care

Reliable machine learning models in genomic medicine using conformal prediction.

Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications have practical benefit in clinical settings. Conformal prediction offers a versatile framework for addressing these concerns by quantifying the uncertainty of predictive models. In this perspective review, we investigate potential applications of conformalized models in genomic medicine and discuss the challenges towards bridging genomic medicine applications with clinical practice. We also demonstrate the impact of a binary transductive model and a regression-based inductive model in predicting drug response as well as the performance of a multi-class inductive predictor in addressing distribution shifts in molecular subtyping. The main conclusion is that as machine learning and genomic medicine are increasingly infiltrating healthcare services, conformal prediction has the potential to overcome the safety limitations of current methods and could be effectively integrated into uncertainty-informed applications within clinical environments

A systematic map and in-depth review of European telehealth interventions efficacy for chronic obstructive pulmonary disease

AbstractBackground: Evidence to support the implementation of telehealth (TH) interventions in the management of chronic obstructive pulmonary disease (COPD) varies throughout Europe. Despite more than ten years of TH research in COPD management, it is still not possible to define which TH interventions are beneficial to which patient group. Therefore, informing policymakers on TH implementation is complicated. We aimed to examine the provision and efficacy of TH for COPD management to guide future decision-making.Methods: A mapping study of twelve systematic reviews of TH interventions for COPD management was conducted. This was followed by an in-depth review of fourteen clinical trials performed in Europe extracted from the systematic reviews. Efficacy outcomes for COPD management were synthesized.Results: The mapping study revealed that systematic reviews with a meta-analysis often report positive clinical outcomes. Despite this, we identified a lack of pragmatic trial design affecting the synthesis of reported outcomes. The in-depth review visualized outcomes for three TH categories, which revealed a plethora of heterogeneous outcomes. Suggestions for reporting within these three outcomes are synthesized as targets for future empirical research reporting.Conclusion: The present study indicates the need for more standardized and updated systematic reviews. Policymakers should advocate for improved TH trial designs, focusing on the entire intervention’s adoption process evaluation. One of the policymakers’ priorities should be the harmonization of the outcome sets, which would be considered suitable for deciding about subsequent reimbursement. We propose possible outcome sets in three TH categories which could be used for discussion with stakeholders.Abstract Background: Evidence to support the implementation of telehealth (TH) interventions in the management of chronic obstructive pulmonary disease (COPD) varies throughout Europe. Despite more than ten years of TH research in COPD management, it is still not possible to define which TH interventions are beneficial to which patient group. Therefore, informing policymakers on TH implementation is complicated. We aimed to examine the provision and efficacy of TH for COPD management to guide future decision-making. Methods: A mapping study of twelve systematic reviews of TH interventions for COPD management was conducted. This was followed by an in-depth review of fourteen clinical trials performed in Europe extracted from the systematic reviews. Efficacy outcomes for COPD management were synthesized. Results: The mapping study revealed that systematic reviews with a meta-analysis often report positive clinical outcomes. Despite this, we identified a lack of pragmatic trial design affecting the synthesis of reported outcomes. The in-depth review visualized outcomes for three TH categories, which revealed a plethora of heterogeneous outcomes. Suggestions for reporting within these three outcomes are synthesized as targets for future empirical research reporting. Conclusion: The present study indicates the need for more standardized and updated systematic reviews. Policymakers should advocate for improved TH trial designs, focusing on the entire intervention’s adoption process evaluation. One of the policymakers’ priorities should be the harmonization of the outcome sets, which would be considered suitable for deciding about subsequent reimbursement. We propose possible outcome sets in three TH categories which could be used for discussion with stakeholders

Interdisciplinary and intersectoral doctoral education designed to improve graduate employability

AbstractTypically, less than half of doctoral graduates will be employed in academia immediately after graduation, with less than 10%-15% achieving a long-term academic career. This leaves 85–90% of PhD graduates seeking employment outside the academic setting, for example in industry and government. The objective of the CHAMELEONS study (CHampioning A Multi-sectoral Education and Learning Experience to Open New pathways for doctoral Students) is to develop innovative educational interventions that shape more adaptable, entrepreneurial, and employable graduates, ready to meet the challenges of the future. Stakeholders from the connected health industry, clinical care, charities, patients, patient representatives, government, recent doctoral graduates, and academics were invited to participate in a “World Café” participatory method for collecting qualitative data. Owing to the COVID–19 health situation this took place via Zoom. Analysis of the results revealed 4 key learning objectives for doctoral graduates to: 1. Develop networking and communication skills. 2. Understand user centred research design. 3. Market research capacity and research skills. 4. Build an understanding of themselves and others. This led to the development of three bespoke doctoral modules: 1. Forging relationships: Building and Sustaining your Doctoral Network; 2. Managing the Project: Keeping on Track with an Eye to the future; Module 3: Starting your Career: Future Proofing your Career and Getting a Job. These modules are available to doctoral students across five European Universities.Abstract Typically, less than half of doctoral graduates will be employed in academia immediately after graduation, with less than 10%-15% achieving a long-term academic career. This leaves 85–90% of PhD graduates seeking employment outside the academic setting, for example in industry and government. The objective of the CHAMELEONS study (CHampioning A Multi-sectoral Education and Learning Experience to Open New pathways for doctoral Students) is to develop innovative educational interventions that shape more adaptable, entrepreneurial, and employable graduates, ready to meet the challenges of the future. Stakeholders from the connected health industry, clinical care, charities, patients, patient representatives, government, recent doctoral graduates, and academics were invited to participate in a “World Café” participatory method for collecting qualitative data. Owing to the COVID–19 health situation this took place via Zoom. Analysis of the results revealed 4 key learning objectives for doctoral graduates to: 1. Develop networking and communication skills. 2. Understand user centred research design. 3. Market research capacity and research skills. 4. Build an understanding of themselves and others. This led to the development of three bespoke doctoral modules: 1. Forging relationships: Building and Sustaining your Doctoral Network; 2. Managing the Project: Keeping on Track with an Eye to the future; Module 3: Starting your Career: Future Proofing your Career and Getting a Job. These modules are available to doctoral students across five European Universities

Challenges and opportunities for telehealth in the management of chronic obstructive pulmonary disease : a qualitative case study in Greece

AbstractBackground: Telehealth (TH) was introduced as a promising tool to support integrated care for the management of chronic obstructive pulmonary disease (COPD). It aims at improving self-management and providing remote support for continuous disease management. However, it is often not clear how TH-supported services fit into existing pathways for COPD management. The objective of this study is to uncover where TH can successfully contribute to providing care for COPD patients exemplified in a Greek care pathway. The secondary objective is to identify what conditions need to be considered for successful implementation of TH services.Methods: Building on a single case study, we used a two-phase approach to identify areas in a Greek COPD care pathway where care services that are recommended in clinical guidelines are currently not implemented (challenges) and areas that are not explicitly recommended in the guidelines but that would benefit from TH services (opportunities). In phase I, we used the care delivery value chain framework to identify the divergence between the clinical guidelines and the actual practice captured by a survey with COPD healthcare professionals. In phase II, we conducted in-depth interviews with the same healthcare professionals based on the discovered divergences. The responses were analyzed with respect to identified opportunities for TH and care pathway challenges.Results: Our results reveal insights in two areas. First, several areas with challenges were identified: patient education, self-management, medication adherence, physical activity, and comorbidity management. TH opportunities were perceived as offering better bi-directional communication and a tool for reassuring patients. Second, considering the identified challenges and opportunities together with other case context details a set of conditions was extracted that should be fulfilled to implement TH successfully.Conclusions: The results of this case study provide detailed insights into a care pathway for COPD in Greece. Addressing the identified challenges and opportunities in this pathway is crucial for adopting and implementing service innovations. Therefore, this study contributes to a better understanding of requirements for the successful implementation of integrated TH services in the field of COPD management. Consequently, it may encourage healthcare professionals to implement TH-supported services as part of routine COPD management.Abstract Background: Telehealth (TH) was introduced as a promising tool to support integrated care for the management of chronic obstructive pulmonary disease (COPD). It aims at improving self-management and providing remote support for continuous disease management. However, it is often not clear how TH-supported services fit into existing pathways for COPD management. The objective of this study is to uncover where TH can successfully contribute to providing care for COPD patients exemplified in a Greek care pathway. The secondary objective is to identify what conditions need to be considered for successful implementation of TH services. Methods: Building on a single case study, we used a two-phase approach to identify areas in a Greek COPD care pathway where care services that are recommended in clinical guidelines are currently not implemented (challenges) and areas that are not explicitly recommended in the guidelines but that would benefit from TH services (opportunities). In phase I, we used the care delivery value chain framework to identify the divergence between the clinical guidelines and the actual practice captured by a survey with COPD healthcare professionals. In phase II, we conducted in-depth interviews with the same healthcare professionals based on the discovered divergences. The responses were analyzed with respect to identified opportunities for TH and care pathway challenges. Results: Our results reveal insights in two areas. First, several areas with challenges were identified: patient education, self-management, medication adherence, physical activity, and comorbidity management. TH opportunities were perceived as offering better bi-directional communication and a tool for reassuring patients. Second, considering the identified challenges and opportunities together with other case context details a set of conditions was extracted that should be fulfilled to implement TH successfully. Conclusions: The results of this case study provide detailed insights into a care pathway for COPD in Greece. Addressing the identified challenges and opportunities in this pathway is crucial for adopting and implementing service innovations. Therefore, this study contributes to a better understanding of requirements for the successful implementation of integrated TH services in the field of COPD management. Consequently, it may encourage healthcare professionals to implement TH-supported services as part of routine COPD management