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A significant worldwide health issue, Type 2 Diabetes Mellitus (T2DM) calls for creative solutions. This in-depth review examines the growing severity of T2DM and the requirement for individualized management approaches. It explores the use of artificial intelligence (AI) in the treatment of diabetes, highlighting its potential for diagnosis, customized treatment plans, and patient self-management. The paper highlights the roles played by AI applications such as expert systems, machine learning algorithms, and deep learning approaches in the identification of retinopathy, the interpretation of clinical guidelines, and prediction models. Examined are difficulties with individualized diabetes treatment, including complex technological issues and patient involvement. The review highlights the revolutionary potential of AI in the management of diabetes and calls for a balanced strategy in which AI supports clinical knowledge. It is crucial to pay attention to ethical issues, data privacy, and joint research initiatives., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Tahir and Farhan.)

Background: During the COVID-19 pandemic, telemedicine was advocated and rapidly scaled up worldwide. However, little is known about for whom this type of care is acceptable., Objective: To examine which patient characteristics (demographic, medical, psychosocial) are associated with telehealth care satisfaction, attitude toward telehealth, and preference regarding telehealth over time in a cardiac patient population., Methods: In total, 317 patients were recruited at the Elisabeth-TweeSteden Hospital in The Netherlands. All patients who had received telehealth care (telephone and video) in the previous 2 months were approached for participation. Baseline, 3-month, and 6-month questionnaires were administered online. A 3-step latent class analysis was conducted to identify trajectories of telehealth use over time and the possible association of the found trajectories with external variables., Results: Five trajectories (classes) were identified for satisfaction with telehealth and 4 for attitude toward telehealth. Patients with higher distress, lower physical and mental health, higher scores on pessimism, and negative affectivity were more likely to be less satisfied. Patients with no partner, more comorbidities, higher distress, lower physical and mental health, and higher scores on pessimism were more likely to hold a negative attitude toward telehealth. For the future application of telehealth, marital status, comorbidities, digital health literacy, and pessimism were significantly related., Conclusion: Results show that patients' profiles should be considered when offering telehealth care and that the "one size fits all" approach does not apply. Results can inform clinical practice on how to better implement remote health care in the future while considering a personalized approach., (© 2023 Heart Rhythm Society.)

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins., Competing Interests: Competing interests: Authors declare no competing interests.

Limited guidance is available for providing patient‐specific care to hypertensive patients, although this chronic condition is the leading risk factor for cardiovascular diseases. To address this issue, we develop an analytical model that takes into account the most relevant risk factors including age, sex, blood pressure, diabetes status, smoking habits, and blood cholesterol. Using the Markov Decision Process framework, we develop a model to maximize expected quality‐adjusted life years, as well as characterize the optimal sequence and combination of antihypertensive medications. Assuming the physician uses the standard medication dose for each drug, and the patient fully adheres to the prescribed treatment regimen, we prove that optimal treatment policies exhibit a threshold structure. Our findings indicate that our recommended thresholds vary by age and other patient characteristics, for example (1) the optimal thresholds for all medication prescription are nonincreasing in age, and (2) the medications need to be prescribed at lower thresholds for males who smoke than for males who have diabetes. The improvements in quality‐adjusted life years associated with our model compare favorably with those obtained by following the British Hypertension Society's guideline, and the gains increase with the severity of risk factors. For instance, in both genders (although at different rates), diabetic patients gain more than non‐diabetic patients. Our sensitivity analysis results indicate that the optimal thresholds decrease if the medications have lower side‐effects and vice versa. [ABSTRACT FROM AUTHOR]

Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com , for open access under a Non Commercial-Share A like 4.0 International License. [ABSTRACT FROM AUTHOR]

Construction rebar workers face postural ergonomic hazards that can lead to work-related Lower Back Disorders (LBDs), primarily due to their prolonged awkward working postures required by the job. In a previous study, Wearable Inertial Measurement Units (WIMUs)-based Personal Protective Equipment (PPE) was developed to alert workers when their trunk inclination holding time exceeded acceptable thresholds as defined in ISO standard 11226:2000. However, subsequent field testing identified PPE was ineffective for some workers because the adopted ISO thresholds were not personalized and did not consider differences in individual’s response to postural ergonomic hazards. To address this problem, this paper introduces a worker-centric method to assist in the self-management of work-related ergonomic hazards, based on data-driven personalized healthcare intervention. Firstly, personalized information is gathered by providing each rebar ironworker a WIMU-based personalized mobile health (mHealth) system to capture their trunk inclination angle and holding time data. Then, the captured individual trunk inclination holding times are analyzed by a Gaussian-like probability density function, where abnormal holding time thresholds can be generated and updated in response to incoming trunk inclination records of an individual during work time. These abnormal holding time thresholds are then adapted to be used as personalized trunk inclination holding time recommendations for an individual worker to self-manage their working postures, based on their own trunk inclination records. The proposed worker-centric method to assist in the self-management of ergonomic postural hazards leading to LBDs was field tested on a construction site over a three-month duration. The results of the paired t -tests indicate that posture scores evaluated by the Ovako Working Posture Analysis System (OWAS) significantly decrease when the personalized recommendation is applied, while increase again when the personalized recommendation is removed. Based on data-driven personalized healthcare intervention, the results demonstrate the significant potential of the proposed worker-centric self-management method for rebar workers in preventing and controlling postural ergonomic hazards during construction rebar ironwork. [ABSTRACT FROM AUTHOR]

Predicting patients’ risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The electric health records (EHRs), which are irregularly sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to the lack of an appropriate representation. Moreover, there needs an effective approach to measure patient similarity on EHRs. In this paper, we propose two novel deep similarity learning frameworks which simultaneously learn patient representations and measure pairwise similarity. We use a convolutional neural network (CNN) to capture local important information in EHRs and then feed the learned representation into triplet loss or softmax cross entropy loss. After training, we can obtain pairwise distances and similarity scores. Utilizing the similarity information, we then perform disease predictions and patient clustering. Experimental results show that CNN can better represent the longitudinal EHR sequences, and our proposed frameworks outperform state-of-the-art distance metric learning methods. [ABSTRACT FROM AUTHOR]

Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases. Early risk identification is central to lifetime health. In this paper, we present a digital health-based platform (PRISM) that leverages artificial intelligence, data visualization and mobile health technologies to empower citizens to self-assess, self-monitor and self-manage their overall risk of major chronic diseases and pursue personalized chronic disease prevention programs. PRISM offers risk assessment tools for 5 chronic conditions, 2, psychiatric disorders and 8 different cancers. [ABSTRACT FROM AUTHOR]

Objective: To develop a Disease and treatment associated Knowledge in RA item bank (DataK-RA) based on item response theory.Methods: Initial items were developed from a systematic review. Rheumatology professionals identified relevant content trough a RAND modified Delphi scoring procedure and consensus meeting. RA patients provided additional content trough a focus group. Patients and professionals rated readability, feasibility and comprehensiveness of resulting items. Cross-sectional data were collected to evaluate psychometric properties of the items.Results: Data of 473 patients were used for item reduction and calibration. Twenty items were discarded based on corrected item-total point biserial correlation <0.30. Confirmatory factor analysis with weighted least squares estimation on the polychoric correlation matrix suggested good fit for a unidimensional model for the remaining 42 items (CFI 0.97 TLI=0.97, RMSEA=0.02, WRMR=0.97), supporting the proposed scoring procedure. Scores were highly reliable and normally distributed with minimal ceiling (1.8%) and no floor effects. 75% of tested hypotheses about the association of DataK-RA scores with related constructs were supported, indicating good construct validity.Conclusion: DataK-RA is a psychometrically sound item bank.Practice Implications: DataK-RA provides health professionals and researchers with a tool to identify and target patients' information needs or to assess effects of educational efforts. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments. [ABSTRACT FROM PUBLISHER]

The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other similar devices over the Internet. The IoT is profoundly redefining the way we create, consume, and share information. Ordinary citizens increasingly use these technologies to track their sleep, food intake, activity, vital signs, and other physiological statuses. This activity is complemented by IoT systems that continuously collect and process environment-related data that has a bearing on human health. This synergy has created an opportunity for a new generation of healthcare solutions. [ABSTRACT FROM PUBLISHER]

Emerging ultrasonic bioelectronics (UBEs) with wearable, implantable or highly integrated forms enable continuous health monitoring and on-demand medical therapy at the point of care, far more superior than traditional ultrasonic medical procedures, and are serving as the technical basis for the state-of-art personalized medicine. The recent progress in emerging UBEs is comprehensively reviewed, with focusing on engineering methodologies and functional applications in personalized healthcare. [Display omitted] Emerging ultrasonic bioelectronics (UBEs) with wearable, implantable or highly integrated forms enable continuous health monitoring and on-demand medical therapy at the point of care, far more superior than traditional ultrasonic medical procedures, and are serving as the technical basis for the state-of-art personalized medicine. However, with the rapid advances in medical ultrasonic technology, a comprehensive overview of these emerging UBEs from addressing engineering challenges in materials and device structures to targeted therapeutic cases is still lacking. Herein, we comprehensively review the recent progress in emerging UBEs, focusing on engineering methodologies and functional applications in personalized healthcare. First, the fundamentals of UBEs is briefed, followed by the latest advances in functional materials critical to the advent of emerging UBEs and the device structural designs for their wearable, implantable and multifunctional integrated applications. Next, exemplary cases of emerging UBEs in terms of their properties and diagnostic, monitoring and therapeutic functional applications based on ultrasonography and ultrasonic wireless powering and communication are highlighted and systematically discussed. Finally, current challenges and opportunities in the field to further improve accuracy, specificity, and system-level integration for personalized healthcare are outlined in detail. [ABSTRACT FROM AUTHOR]

Smartphone-based devices and applications (SBDAs) with cost effectiveness and remote sensing are the most promising and effective means of delivering mobile healthcare (mHealthcare). Several SBDAs have been commercialized for the personalized monitoring and/or management of basic physiological parameters, such as blood pressure, weight, body analysis, pulse rate, electrocardiograph, blood glucose, blood glucose saturation, sleeping and physical activity. With advances in Bluetooth technology, software, cloud computing and remote sensing, SBDAs provide real-time on-site analysis and telemedicine opportunities in remote areas. This scenario is of utmost importance for developing countries, where the number of smartphone users is about 70% of 6.8 billion cell phone subscribers worldwide with limited access to basic healthcare service. The technology platform facilitates patient-doctor communication and the patients to effectively manage and keep track of their medical conditions. Besides tremendous healthcare cost savings, SBDAs are very critical for the monitoring and effective management of emerging epidemics and food contamination outbreaks. The next decade will witness pioneering advances and increasing applications of SBDAs in this exponentially growing field of mHealthcare. This article provides a critical review of commercial SBDAs that are being widely used for personalized healthcare monitoring and management.

'Value-based' outcomes, pricing, and reimbursement are widely discussed as health sector reforms these days. In this paper, we discuss their meaning and relationship in the context of personalized healthcare, defined as receipt of care conditional on the results of a biomarker-based diagnostic test. We address the question: "What kinds of pricing and reimbursement models should be applied in personalized healthcare?" The simple answer is that competing innovators and technology adopters should have incentives that promote long-term dynamic efficiency. We argue that--to meet this social objective of optimal innovation in personalized healthcare--payers, as agents of their plan participants, should aim to send clear signals to their suppliers about what they value. We begin by revisiting the concept of value from an economic perspective, and argue that a broader concept of value is needed in the context of personalized healthcare. We discuss the market for personalized healthcare and the interplay between price and reimbursement. We close by emphasizing the potential barrier posed by inflexible or cost-based reimbursement systems, especially for biomarker-based predictive tests, and how these personalized technologies have global public goods characteristics that require global value-based differential pricing to achieve dynamic efficiency in terms of the optimal rate of innovation and adoption. [ABSTRACT FROM AUTHOR]

In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented, and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions. [ABSTRACT FROM AUTHOR]

Self-tracking devices point to a future in which individuals will be more involved in the management of their health and will generate data that will benefit clinical decision making and research. They have thus attracted enthusiasm from medical and public health professionals as key players in the move toward participatory and personalized healthcare. Critics, however, have begun to articulate a number of broader societal and ethical concerns regarding self-tracking, foregrounding their disciplining, and disempowering effects. This paper has two aims: first, to analyze some of the key promises and concerns that inform this polarized debate. I argue that far from being solely about health outcomes, this debate is very much about fundamental values that are at stake in the move toward personalized healthcare, namely, the values of autonomy, solidarity, and authenticity. The second aim is to provide a framework within which an alternative approach to self-tracking for health can be developed. I suggest that a practice-based approach, which studies how values are enacted in specific practices, can open the way for a new set of theoretical questions. In the last part of the paper, I sketch out how this can work by describing various enactments of autonomy, solidarity, and authenticity among self-trackers in the Quantified Self community. These examples show that shifting attention to practices can render visible alternative and sometimes unexpected enactments of values. Insofar as these may challenge both the promises and concerns in the debate on self-tracking for health, they can lay the groundwork for new conceptual interventions in future research. [ABSTRACT FROM AUTHOR]

Background and objectives In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. Methods In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. Results In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90–95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. Conclusions The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making. [ABSTRACT FROM AUTHOR]

This study investigates the sensitivity and specificity of predicting epileptic seizures from intracranial electroencephalography (iEEG). A monitoring system is studied to generate an alarm upon detecting a precursor of an epileptic seizure. The iEEG traces of ten patients suffering from medically intractable epilepsy were used to build a prediction model. From the iEEG recording of each patient, power spectral densities were calculated and classified using support vector machines. The prediction results varied across patients. For seven patients, seizures were predicted with 100% sensitivity without any false alarms. One patient showed good sensitivity but lower specificity, and the other two patients showed lower sensitivity and specificity. Predictive analytics based on the spectral feature of iEEG performs well for some patients but not all. This result highlights the need for patient-specific prediction models and algorithms. [ABSTRACT FROM AUTHOR]

Skin-interfaced electronics (skintronics) have received considerable attention due to their thinness, skin-like mechanical softness, excellent conformability, and multifunctional integration. Current advancements in skintronics have enabled health monitoring and digital medicine. Particularly, skintronics offer a personalized platform for early-stage disease diagnosis and treatment. In this comprehensive review, we discuss (1) the state-of-the-art skintronic devices, (2) material selections and platform considerations of future skintronics toward intelligent healthcare, (3) device fabrication and system integrations of skintronics, (4) an overview of the skintronic platform for personalized healthcare applications, including biosensing as well as wound healing, sleep monitoring, the assessment of SARS-CoV-2, and the augmented reality-/virtual reality-enhanced human-machine interfaces, and (5) current challenges and future opportunities of skintronics and their potentials in clinical translation and commercialization. The field of skintronics will not only minimize physical and physiological mismatches with the skin but also shift the paradigm in intelligent and personalized healthcare and offer unprecedented promise to revolutionize conventional medical practices. [Display omitted] [ABSTRACT FROM AUTHOR]

Soft bioelectronics play an increasingly crucial role in high-precision therapeutics due to their softness, biocompatibility, clinical accuracy, long-term stability, and patient-friendliness. In this review, we provide a comprehensive overview of the latest representative therapeutic applications of advanced soft bioelectronics, ranging from wearable therapeutics for skin wounds, diabetes, ophthalmic diseases, muscle disorders, and other diseases to implantable therapeutics against complex diseases, such as cardiac arrhythmias, cancer, neurological diseases, and others. We also highlight key challenges and opportunities for future clinical translation and commercialization of soft therapeutic bioelectronics toward personalized medicine.

In this work, we investigated the technical feasibility of 'on-demand' production of selected drugs to cover their demand for a time window of 90 days. We focused on two sub-processes 'automated chemical synthesis' and 'formulation in micropellets'  to enable personalized dosing. The production of drugs 'on-demand' is challenging, important, but also attractive. Switzerland could thus gain access to an additional instrument for increasing resilience for supply-critical drugs. The biggest challenge in the case study presented here is the scalability of automated chemical synthesis and the application range of micropellet formulations., (Copyright 2023 Daniel Gygax, Kaspar Eigenmann, Christian Suter, Marianne Hürzeler, Ahmed Mahmoud, Johannes Mosbacher, Norbert Pöllinger. License: This work is licensed under a Creative Commons Attribution 4.0 International License.)

This paper presents a solution for monitoring of solar ultraviolet (UV) exposure and alerting about risks in real time. The novel system provides smart personalized indications for solar radiation protection. The system consists of a sensing device and a mobile application. The sensing device monitors solar radiation in real time and transmits the values wirelessly to a smart device, in which the mobile application is installed. Then, the mobile application processes the values from the sensory apparatus, based on a fuzzy expert system (FES) created from personal information (hair and eye color, tanning and burning frequency), which are entered by the user answering a short questionnaire. The FES provides an estimation of the recommended time of safe exposure in direct sunlight. The proposed system is designed to be portable (a wearable sensing device and smartphone) and low cost, while supporting multiple users.