Search

Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain. [ABSTRACT FROM AUTHOR]

Abstract Advancing age is associated with declines in cognitive function. Although physical activity is thought to protect against this decline, it is unclear how a short-term uptake in daily steps or a decline in day-to-day step variability may contribute to cognition among older adults. We tested associations between changes in step counts, day-to-day step variability and executive cognitive functions among older adults taking part in a physical activity intervention. Thirty-seven older adults (33 females; 71.4 ± 6.3 years) completed a 10-week personalized physical activity intervention. Participants wore a Fitbit to measure daily step counts throughout the study. They also completed a computerized Stroop task before and after the intervention. Average step counts and step count variability via average-real-variability (ARV) were determined. Compared to pre-intervention, step counts increased (p

This article explores the concept of personalized health and precision medicine, arguing that these terms are often used as buzzwords and can be misleading. It discusses various aspects of personalized health and precision medicine, including infrastructure, human labor, and political dimensions. The authors emphasize the need for further scrutiny and understanding of the specific requirements for personalized health and precision medicine. They advocate for diverse perspectives and the involvement of various actors in its development, as well as the consideration of social, economic, psychological, and biological determinants of health. The article concludes by advocating for grassroots-driven initiatives and the integration of social studies with biomedical sciences to challenge hierarchical structures in healthcare. [Extracted from the article]

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors. [ABSTRACT FROM AUTHOR]

Personalized digital health systems (pHealth) bring together in sharp juxtaposition very different yet hopefully complementary moral principles in the shared objectives of optimizing health care and the health status of individual citizens while maximizing the application of robust clinical evidence through harnessing powerful and often complex modern data-handling technologies. Principles brought together include respecting the confidentiality of the patient–clinician relationship, the need for controlled information sharing in teamwork and shared care, benefitting from healthcare knowledge obtained from real-world population-level outcomes, and the recognition of different cultures and care settings. This paper outlines the clinical process as enhanced through digital health, reports on the examination of the new issues raised by the computerization of health data, outlines initiatives and policies to balance the harnessing of innovation with control of adverse effects, and emphasizes the importance of the context of use and citizen and user acceptance. The importance of addressing ethical issues throughout the life cycle of design, provision, and use of a pHealth system is explained, and a variety of situation-relevant frameworks are presented to enable a philosophy of responsible innovation, matching the best use of enabling technology with the creation of a culture and context of trustworthiness.

BackgroundNew health technologies and data offer tailored prevention and spot-on treatments, which can considerably reduce healthcare costs. In healthy individuals, insurers can participate in the creation of health capital through data and preventing the occurrence of a disease. In the onset of a disease, sequencing an individual's genome can provide information leading to the use of more efficient treatments. Both improvements are at the core of the “personalized health” paradigm. As a positive side effect, a reduction in healthcare costs is expected. However, the integration of personalized health in insurance schemes starts with a closer understanding of the demand drivers.MethodsUsing novel data from a survey carried out in Switzerland, we determine the factors influencing the uptake and sharing of data from genetic tests. In our regression analyses, we use five sets of socioeconomic, lifestyle, health insurance, sentiment, and political beliefs variables. Furthermore, two framings assess the willingness to undertake a test and the readiness to share results with an insurer when the costs of the test are borne by the insurer or the individual.ResultsWe find that socioeconomic, lifestyle, or political belief variables have very little influence on the uptake of tests and the sharing of data. On the contrary, our results indicate that sentiment and insurance factors play a strong role. More precisely, if genetic tests are perceived as a mean to perform health prevention, this pushes individuals to take them. Furthermore, using the insurer's smartphone app leads to an increase of the likelihood to undergo a test and doubles the probability to share related data. Regarding insurance plans and deductible levels, there is no strong correlation neither with the willingness to take a test nor to share the data. Finally, individuals with complementary health insurance plans are less likely to share results. From the framings for the payment of genetic tests, our results indicate a positive effect of the insurer as a payer on the willingness to undertake tests as well as on data sharing.ConclusionOur results lay the ground for a deeper understanding of the role of payers on health decisions and sharing of health-related data. In particular, we find that it is relevant for health insurers to engage with their clients.

The US National Human Genome Research Institute defines precision medicine as follows: “Precision medicine (generally considered analogous to personalized medicine or individualized medicine) is an innovative approach that uses information about an individual's genomic, environmental, and lifestyle information to guide decisions related to their medical management. The goal of precision medicine is to provide a more precise approach for the prevention, diagnosis, and treatment of disease.” In this perspective article, we question this definition of precision medicine and the risks linked to its current practice and development. We highlight that in practice, precision medicine is based on the use of large volumes of biological data for individual purposes mostly in line with the biomedical model of health, which carries the risk of the biological reductionism of the person. A more comprehensive, precise, and even “personal” approach to health would require taking into account environmental, socio-economic, psychological, and biological determinants, an approach more in line with the biopsychosocial model of health. The role of environmental exposures, in a broad sense, is highlighted more and more, notably in the field of exposome research. Not considering the conceptual framework in which precision medicine is deployed leads to the concealment of the different responsibilities that can be mobilized within the health system. Anchoring precision medicine in a model that does not limit its definition to its biological and technical components makes it possible to envisage a personalized and more precise medicine, integrating a greater share of interventions centered on the skills and life contexts of individuals.

The natural barrier that separates us from our surroundings is our skin. It enables us to experience a range of forms of sensations, temperature variations, and different levels of touch pressure. Because human skin is so instinctive, it's simple to overlook the intricacy of the body's primary sensory organ. Our skin contains a variety of diverse, highly specialized sense receptors that enable it to perceive functions from our surroundings efficiently and securely with great precision. These receptors, by making use of an integrated flexible system, convert physical contact information into electrical impulses before sending it to the nervous system for further involved interpretation. Electronic skin, i.e., E-skin also referred to as e-skin or synthetic skin, is a very thin electrical material made by fusing electronic components to elastic, pliable substrates that are made with efforts of mimicking the potentials of human skin. E-skin is a cutting-edge technology that is largely used for medicinal purposes, such as detecting and perceiving internal life sign processes in living beings, as well as their unprecedented monitoring. To explore the aspects of E-skin for health wellness, this article explores recent advancements in different E-skin technologies, properties of the materials used in e-skin fabrication, and its future potential.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Behavioral interventions (such as those developed to increase physical activity, achieve smoking cessation, or weight loss) can be represented as dynamic process systems incorporating a multitude of factors, ranging from cognitive (internal) to environmental (external) influences. This facilitates the application of system identification and control engineering methods to address questions such as: what drives individuals to improve health behaviors (such as engaging in physical activity)? In this paper, the goal is to efficiently estimate personalized, dynamic models which in turn will lead to control systems that can optimize this behavior. This problem is examined in system identification applied to the Just Walk study that aimed to increase walking behavior in sedentary adults. The paper presents a Discrete Simultaneous Perturbation Stochastic Approximation (DSPSA)-based modeling of the Goal Attainment construct estimated using AutoRegressive with eXogenous inputs (ARX) models. Feature selection of participants and ARX order selection is achieved through the DSPSA algorithm, which efficiently handles computationally expensive calculations. DSPSA can search over large sets of features as well as regressor structures in an informed, principled manner to model behavioral data within reasonable computational time. DSPSA estimation highlights the large individual variability in motivating factors among participants in Just Walk , thus emphasizing the importance of a personalized approach for optimized behavioral interventions. [Display omitted] This paper demonstrates a comprehensive data-driven methodology for modeling physical activity through the synergism of behavior change theory, process modeling, and control. • Increasing physical activity in individuals can be described as a process system. • Behavioral science, process modeling, and system identification can be applied. • Benefits are obtained from novel experiment design and model validation procedures. • Stochastic search via DSPSA enables determining the best features in limited time. • Estimated models can contribute to personalized "just in time" adaptive interventions. [ABSTRACT FROM AUTHOR]

All health-related issues exist in a context of extending health expectancy. Behavioral risk factors, diagnostic ”omics,” disparities, insurance, tissue engineering, and climate can shorten life expectancy, but before that, health expectancy. Longer life can bring decades of disability; longer health can mean dying healthy after brief incapacity. Because health precedes other accomplishments, extending average health expectancy into the ninth decade during the 21st century would have an impact comparable to doubling life expectancy in the 20th century.

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F 1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.

A core aspect of the entrepreneurial hospital is the mobilisation of the means of care beyond care itself. In previous work, we showed how the entrepreneurial hospital uses its unique access to patient populations, whose health needs make them available, in order to facilitate research into therapeutic, diagnostic, or service delivery innovation. It ‘entrepreneurialises’ care, we argued, to meet research needs. What may be less obvious in this process, however, is that research, too, is entrepreneurialised to meet care needs. That is, the entrepreneurial hospital not only constitutes its patient populations and care infrastructure as distinctive assets that serve its entrepreneurial aims, but also positions its entrepreneurial aims as a decisive element in the service of care. This article develops the concept of the entrepreneurial hospital to help theorise biobanking. It foregrounds the views of biobankers – drawing from our ethnographic research and especially our interviews with key-informants (2008-2009) who work in some relation to biobanking in a Canadian province – thereby providing a window onto an important, yet under-examined, set of rationales motivating the entrepreneurial integration of care and research through biobanks.

This paper explores enactments of participation in two Swiss biobanking configurations, a cohort biobank and a general biobank. It sheds light on the role of Personalized Health endeavours, in which biobanks play a crucial role. In order to contribute to the understanding of the role of participation in biomedical research dynamics, the analysis focuses on the processes of co-production of identity and biobanks (Tupasela et al. 2015). It documents the overlaps between the population – providers of biological samples – and the public, the collective who is expected to give its opinion on issues raised by the reconfiguration of the research/healthcare interface. It shows that modalities of participation impact the potential scientific value derived from the biobank’s population, but also that the reconfiguration of the research/healthcare interface at the core of biobanking contributes to the current blooming of discourses and practices of participation. It argues that the forms of collective identity shaped through participation as population and/or public, exceed formal strategies of participatory governance and may play an even more important role in the shaping of biobanking configurations.

Introduction: Currently, the deployment of human–computer interactive technologies to provide personalized care has grown and immensely taken shape in most healthcare settings. With the increasing growth of the internet and technology, personalized health interventions including smartphones, associated apps, and other interventions demonstrate prowess in various health fields, including cardiovascular management. This systematic review thus examines the effectiveness of various human–computer interactions technologies through telehealth (mainly eHealth) towards optimizing the outcomes in cardiovascular treatment. Methods: A comprehensive search of MEDLINE, EMBASE, and CINAHL databases using key terms was conducted from 2000 to November 2021 to identify suitable studies that explored the use of human–computer interaction technologies to provide a personalized care approach to facilitate bolstered outcomes for cardiovascular patients, including the elderly. The included studies were assessed for quality and risk of bias, and the authors undertook a data extraction task. Results: Ten studies describing the use of a mix of personalized health app (mHealth) interventions were identified and included in the study. Among the included studies, nine of them were randomized trials. All of the studies demonstrated the effectiveness of various personalized health interventions in maximizing the benefits of cardiovascular disease treatment. Conclusions: Personalized health application interventions through precision medicine has great potential to boost cardiovascular disease management outcomes, including rehabilitation. Fundamentally, since each intervention’s focus might differ based on the disease and outcome preference, it is recommended that more research be done to tailor the interventions to specific disease and patient outcome expectations.

Genetic research is advancing rapidly. One important area for the application of the results from this work is personalized health. These are treatments and preventive interventions tailored to the genetic profile of specific groups or individuals. The inclusion of personalized health in existing health systems is a challenge for policymakers. In this article, we present the results of a thematic scoping review of the literature dealing with governance and policy of personalized health. Our analysis points to four governance challenges that decisionmakers face against the background of personalized health. First, researchers have highlighted the need to further extend and harmonize existing research infrastructures in order to combine different types of genetic data. Second, decisionmakers face the challenge to create trust in personalized health applications, such as genetic tests. Third, scholars have pointed to the importance of the regulation of data production and sharing to avoid discrimination of disadvantaged groups and to facilitate collaboration. Fourth, researchers have discussed the challenge to integrate personalized health into regulatory-, financing-, and service provision structures of existing health systems. Our findings summarize existing research and help to guide further policymaking and research in the field of personalized health governance.

Genetic research is advancing rapidly. One important area for the application of the results from this work is personalized health. These are treatments and preventive interventions tailored to the genetic profile of specific groups or individuals. The inclusion of personalized health in existing health systems is a challenge for policymakers. In this article, we present the results of a thematic scoping review of the literature dealing with governance and policy of personalized health. Our analysis points to four governance challenges that decisionmakers face against the background of personalized health. First, researchers have highlighted the need to further extend and harmonize existing research infrastructures in order to combine different types of genetic data. Second, decisionmakers face the challenge to create trust in personalized health applications, such as genetic tests. Third, scholars have pointed to the importance of the regulation of data production and sharing to avoid discrimination of disadvantaged groups and to facilitate collaboration. Fourth, researchers have discussed the challenge to integrate personalized health into regulatory-, financing-, and service provision structures of existing health systems. Our findings summarize existing research and help to guide further policymaking and research in the field of personalized health governance. [ABSTRACT FROM AUTHOR]

Background: The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology., Objective: In this scoping review, we aimed to (1) identify the different areas of women's health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women's adoption and use of connected health devices., Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting., Results: We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women's use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior., Conclusions: Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women's health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women's health research and highlights opportunities for digital technology development in other women's health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women's personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women's health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result., International Registered Report Identifier (irrid): RR2-10.2196/26110., (©Jalisa Lynn Karim, Rachel Wan, Rhea S Tabet, Derek S Chiu, Aline Talhouk. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.05.2024.)

Advancing age is associated with declines in cognitive function. Although physical activity is thought to protect against this decline, it is unclear how a short-term uptake in daily steps or a decline in day-to-day step variability may contribute to cognition among older adults. We tested associations between changes in step counts, day-to-day step variability and executive cognitive functions among older adults taking part in a physical activity intervention. Thirty-seven older adults (33 females; 71.4 ± 6.3 years) completed a 10-week personalized physical activity intervention. Participants wore a Fitbit to measure daily step counts throughout the study. They also completed a computerized Stroop task before and after the intervention. Average step counts and step count variability via average-real-variability (ARV) were determined. Compared to pre-intervention, step counts increased (p < 0.001) and step variability decreased post-intervention (p = 0.04). Models describing the changes in step counts and ARV over the 10-weeks were cubic (both, p < 0.04). Reaction times during the simple (p = 0.002) and switching (p = 0.04) conditions were faster post-intervention. Change in step variability was positively associated with the change in reaction time for the switching condition (β = 0.029, p = 0.002). On average, a reduction in day-to-day step variability was associated with improvements in cognitive flexibility., (© 2024. The Author(s).)

Background: There is a growing need to structurally change the way chronic illness care is organized as health systems struggle to meet the demand for chronic care. mHealth technologies can alter traditional approaches to health care provision by stimulating self-management of chronically ill patients. The aim of this study was to understand the complex environment related to the introduction of mHealth solutions into primary care for chronic disease management while considering health system functioning and stakeholder views.Methods: A transdisciplinary approach was used informed by the Interactive Learning and Action (ILA) methodology. Exploratory interviews (n = 5) were held with representatives of stakeholder groups to identify and position key stakeholders. Subsequently, professionals and chronically ill patients were consulted separately to elaborate on the barriers and facilitators in integration, using semi-structured interviews (n = 17) and a focus group (n = 6). Follow-up interviews (n = 5) were conducted to discuss initial findings of the stakeholder analysis.Results: Most stakeholders, in particular primary care practitioners and patients, seem to have a supporting or mixed attitude toward integration of mHealth. On the other hand, several powerful stakeholders, including primary care information system developers and medical specialists are likely to show resistance or a lack of initiative toward mHealth integration. Main barriers to mHealth integration were a lack of interoperability with existing information systems; difficulties in financing mHealth implementation; and limited readiness in general practices to change. Potential enablers of integration included co-design of mHealth solutions and incentives for pioneers.Conclusion: Stakeholders acknowledge the benefits of integrating mHealth in primary care. However, important barriers perceived by end-users prevent them to fully adopt and use mHealth. This study shows that the complexity of introducing mHealth into primary care calls for strategies encouraging collaboration between multiple stakeholders to enhance successful implementation.

Abstract The use of information and communication technologies for health (eHealth) delivered via mobile-based or digitally enhanced solutions (mHealth) have rapidly evolved. When used together across various mobile applications and devices eHealth and mHealth technologies have the ability to passively monitor behavior as an indicator of socialization and mood; accumulate a range of biomedical data such as weight and heart rate; and track metrics associated with activities including steps taken and hours slept. Yet, these technologies are insufficient for measuring the full array of data about an individual and the impact of that data on a person’s current and future health. Digital health converges eHealth and mHealth with patient data about their health, healthcare, living, and environment with genomics. An innovative opportunity to unravel the complexities of disease and aging is increasingly possible with an integrative multi-omics approach informed by multidisciplinary sciences including medicine, design, biomedical informatics and engineering. The digitization of individual level data from all available sources makes possible the development of DigitalMe™, a personalized virtual avatar of a real person. The combination of longitudinally collected person generated data and molecular data derived from biospecimens offers researchers unique opportunities to better understand the mechanisms of disease while advancing person-centric hypotheses generation related to treatments, diagnostics, and prognostics.

Abstract Background A key feature of metabolic health is the ability to adapt upon dietary perturbations. A systemic review defined an optimal nutritional challenge test, the “PhenFlex test” (PFT). Recently, it has been shown that the PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Furthermore, it was demonstrated that quantification of PFT response was more sensitive as compared to fasting markers in demonstrating reduced phenotypic flexibility in metabolically impaired type 2 diabetes subjects. Methods This study aims to demonstrate that quantification of PFT response can discriminate between different states of health within the healthy range of the population. Therefore, 100 healthy subjects were enrolled (50 males, 50 females) ranging in age (young, middle, old) and body fat percentage (low, medium, high), assuming variation in phenotypic flexibility. Biomarkers were selected to quantify main processes which characterize phenotypic flexibility in response to PFT: flexibility in glucose, lipid, amino acid and vitamin metabolism, and metabolic stress. Individual phenotypic flexibility was visualized using the “health space” by representing the four processes on the health space axes. By quantifying and presenting the study subjects in this space, individual phenotypic flexibility was visualized. Results Using the “health space” visualization, differences between groups as well as within groups from the healthy range of the population can be easily and intuitively assessed. The health space showed a different adaptation to the metabolic PhenFlex test in the extremes of the recruited population; persons of young age with low to normal fat percentage had a markedly different position in the health space as compared to persons from old age with normal to high fat percentage. Conclusion The results of the metabolic PhenFlex test in conjunction with the health space reliably assessed health on an individual basis. This quantification can be used in the future for personalized health quantification and advice.

Consumer and system-wide gains remain limited by an outmoded policy regime. With scientific innovation running far ahead of public policy, physicians, researchers and patients are not receiving full advantage of the latest developments. European health systems require a seamless and rapid flow of digital information, including genomic, clinical outcome, and claims data. Research derived from clinical care must feed back into assessment, in order to advance care quality for consumers. National health systems are heterogeneous; the solutions and required fundamental approaches differ between the European member states and are not entirely portable and scalable. To date, this applies not only to general systemic aspects but particularly to cross-border reimbursement issues and the exchange of treatment and patient data. To answer those needs, an international consortium was established to implement the project "Interregional coordination for a fast and deep uptake of personalised health": Regions4PerMed. A cycle of international events, such as conferences, in situ visits and workshops, has been planned. Interdisciplinary groups of experts will exchange thoughts and experiences to design solutions that could be implemented in the various healthcare systems. Regions4PerMed aims to coordinate regional policies and innovation programmes in personalised medicine and personalised health to accelerate the deployment of personalised health for patients. Key Action 2 is dedicated to health technologies and smart and integrated care. [ABSTRACT FROM AUTHOR]

Switzerland has been a pioneer in the field of bioinformatics since the early 1980s. As time passed, the need for one entity to gather and represent bioinformatics on a national scale was felt and, in 1998, the SIB Swiss Institute of Bioinformatics was created. Hence, 2018 marks the Institute's 20th anniversary. Today, the Institute federates 65 research and service groups across the country—whose activity domains range from genomics, proteomics, medicine and health to structural biology, systems biology, phylogeny and evolution—and a group whose sole task is dedicated to training. The Institute hosts 12 competence centres that provide bioinformatics and biocuration expertise to life scientists across the country. SIB sensed early on that the wealth of data produced by modern technologies in medicine and the growing self-awareness of patients was about to revolutionize the way medical data are considered. In 2012, it created a Clinical Bioinformatics group to address the issue of personalized health, thus working towards a more global approach to patient management, and more targeted and effective therapies. In this respect, SIB has a major role in the Swiss Personalized Health Network to make patient-related data available to research throughout the country. The uniqueness of the Institute's governance structure has also inspired the structure of other European life science organizations, notably ELIXIR. [ABSTRACT FROM AUTHOR]

Background: Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population., Objective: This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis., Methods: A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged., Results: The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly., Conclusions: With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine., (©Tobias Ueli Blatter, Harald Witte, Jules Fasquelle-Lopez, Jean Louis Raisaro, Alexander Benedikt Leichtle. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.10.2023.)

The objective was to assess risk of hospitalization and mortality of comorbidities using divisive hierarchical risk clustering to advice clinical interventions. Subjects and Methods : Data from the EHR of a general population, 3799885 adults, followed by 5 years. Model were performed using Spark and Scikit-learn and accuracy for the models was analyzed. Results : The number of models generated depends in part on the number of chronic diseases included (ex testing a sample of six diseases, a total number of 397 models for all-cause mortality and 431 models for hospitalization). The estimated models offered an ordered selection for the relevant clinical variables and their estimated risk as a group and for the individual patient in the group. Accuracy was assessed according to age, sex and the cardinality of the comorbid groups. A mobile version and dashboard were developed. Conclusion : The software developed stratified hospital admission and mortality risk in clusters of chronic diseases, and for a given patient, it could advise intensifying treatment or reallocating the patient risk., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: A key feature of metabolic health is the ability to adapt upon dietary perturbations. A systemic review defined an optimal nutritional challenge test, the "PhenFlex test" (PFT). Recently, it has been shown that the PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Furthermore, it was demonstrated that quantification of PFT response was more sensitive as compared to fasting markers in demonstrating reduced phenotypic flexibility in metabolically impaired type 2 diabetes subjects. Methods: This study aims to demonstrate that quantification of PFT response can discriminate between different states of health within the healthy range of the population. Therefore, 100 healthy subjects were enrolled (50 males, 50 females) ranging in age (young, middle, old) and body fat percentage (low, medium, high), assuming variation in phenotypic flexibility. Biomarkers were selected to quantify main processes which characterize phenotypic flexibility in response to PFT: flexibility in glucose, lipid, amino acid and vitamin metabolism, and metabolic stress. Individual phenotypic flexibility was visualized using the "health space" by representing the four processes on the health space axes. By quantifying and presenting the study subjects in this space, individual phenotypic flexibility was visualized. Results: Using the "health space" visualization, differences between groups as well as within groups from the healthy range of the population can be easily and intuitively assessed. The health space showed a different adaptation to the metabolic PhenFlex test in the extremes of the recruited population; persons of young age with low to normal fat percentage had a markedly different position in the health space as compared to persons from old age with normal to high fat percentage. Conclusion: The results of the metabolic PhenFlex test in conjunction with the health space reliably assessed health on an individual basis. This quantification can be used in the future for personalized health quantification and advice. [ABSTRACT FROM AUTHOR]

New technologies in life sciences are helping to revolutionize health management with the objective to move towards personalized healthcare. The Swiss Federal government is investing in multidisciplinary, countrywide initiatives such as Health 2030 to meet this challenge.

Crohn’s disease is a chronic pathology belonging to the group of inflammatory bowel diseases. Patients suffering from Crohn’s disease must be supervised by a medical specialist for the rest of their lives; furthermore, each patient has its own characteristics and is affected by the disease in a different way, so health recommendations and treatments cannot be generalized and should be individualized for a specific patient. To achieve this personalization in a cost-effective way using technology, we propose a model based on different information flows: control, personalization, and monitoring. As a result of the model and to perform a functional validation, an architecture based on services and a prototype of the system has been defined. In this prototype, a set of different devices and technologies to monitor variables from patients and their environment has been integrated. Artificial intelligence algorithms are also included to reduce the workload related to the review and analysis of the information gathered. Due to the continuous and automated monitoring of the Crohn’s patient, this proposal can help in the personalization of the Crohn’s disease clinical process. [ABSTRACT FROM AUTHOR]