Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors.

The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects' diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.

[Application of Artificial Intelligence Models in Nursing Research].

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care.

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.

Precision nursing and intermittent pneumatic compression significantly reduce perioperative deep vein thrombosis in post-surgical ovarian cancer patients.

OBJECTIVE: This study investigated the efficacy of precision nursing combined with intermittent pneumatic compression (IPC) devices in preventing perioperative deep vein thrombosis (DVT) in patients with ovarian cancer. METHODS: A retrospective analysis was conducted on 136 ovarian cancer surgery patients at Xi'an People's Hospital from February 2019 to April 2023. The patients were divided into two groups: 71 patients received precision nursing with IPC intervention (study group), while the remaining received standard nursing care (control group). Key variables analyzed included operation duration, intraoperative blood loss, postoperative blood transfusion requirements, changes in limb circumference, and variations in coagulation parameters activated partial thromboplastin time (APTT), D-Dimer (D-D), Fibrinogen (FIB), and Prothrombin Time (PT) before and after surgery. The incidence of DVT was recorded in both groups to determine risk factors for deep vein thrombosis. RESULTS: No significant differences were observed between the groups regarding operation duration, intraoperative blood loss, and postoperative blood transfusion rates (P > 0.05). Post-intervention, significant improvements were noted in the study group, with reduced FIB and D-D levels and increased PT and APTT levels compared to the control group (P < 0.05). Furthermore, the study group exhibited a significantly smaller post-intervention difference in limb circumference and a lower incidence of DVT (P=0.003). Precision nursing combined with IPC, pre-intervention D-D < 498.5, and FIGO stages III+IV were identified as independent factors against DVT development. CONCLUSION: Precision nursing paired with an IPC device significantly reduces the risk of perioperative DVT in ovarian cancer patients compared to conventional care.

Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.

PURPOSE: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. METHODS: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. RESULTS: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, year of diagnosis, age, proximity to superfund sites, and primary payer. Spatio-temporal clusters highlighted geographic areas with a statistically significant high probability of late-stage diagnoses, emphasizing the need for targeted healthcare interventions. CONCLUSIONS: This research underlines the potential of ML in enhancing the prognostic predictions in oncology, particularly in CRC. The gradient boosting model, with its robust performance, holds promise for deployment in healthcare systems to aid early detection and formulate localized cancer prevention strategies. The study's methodology demonstrates a significant step toward utilizing AI in public health to mitigate disparities and improve cancer care outcomes.

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms.

Introduction: Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has. Methods: We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals. Results: Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention. Discussion: We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed. Systematic Review Registration: Identifier: CRD42022357408.

Model-Driven Dementia Prevention and Intervention Platform.

Most types of dementia, including Alzheimer's disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems).

The development of an evidence-informed Convergent Care Theory: Working together to achieve optimal health outcomes.

BACKGROUND: Healthcare is a complex and divergent system with uncertainty, unpredictability, and multi-layered stakeholders. The relationships among the stakeholders are multifaceted and dynamic, requiring continual interpersonal connections, networks, and co-evolution. It is pivotal to have an evidence-informed theory to explain the phenomenon, uniting the multifaceted stakeholders' efforts. PURPOSE: To describe the development of an evidence-informed theory, the Convergent Care Theory, assembling healthcare stakeholders to work together and achieve optimal health outcomes. METHODS: The Convergent Care Theory was developed using a theory synthesis approach based on empirical research and literature reviews published by the theory-proposing author. The empirical evidence was categorized into: patients and families, healthcare providers, healthcare organizations, and patients' and healthcare providers' self-care. RESULTS: The Convergent Care Theory includes four concepts: all-inclusive organizational care , healthcare professional collaborative care, person-centered precision care, and patients ' and healthcare providers' self-care. Achieving convergent care is a process requiring all stakeholders to work together. Six major facilitators emerged from the research evidence: competence, compassion, accountability, trusting, sharing, and engaging. CONCLUSION: This article introduced the development process of the evidence-informed Convergent Care Theory. Healthcare systems are complex, with multiple stakeholders' needs to meet. The Convergent Care Theory strives to unite healthcare stakeholders, bond resources, and join forces to achieve optimal healthcare outcomes. The underpinning of the theory is a caring culture, which is an underlying code for organizational and team behaviors and the foundation of optimal health outcomes.

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanations for the predictions of such a mobile application called DemPredict using methods from the field of Explainable Artificial Intelligence (XAI). In order to evaluate which method is best suited, different XAI approaches are used and compared. However, the comparability of the results is a key challenge. By evaluating the trustworthiness, stability, and computation time of the methods, it is possible to identify the optimal XAI approaches for the respective algorithms.

Increases in multimorbidity with weight class in the United States.

Little is known regarding how multimorbidity combinations associated with obesity change with increase in body weight. This study employed data from the national Cerner HealthFacts Data Warehouse to identify changes in multimorbidity patterns by weight class using network analysis. Networks were generated for 154 528 middle-aged patients in the following categories: normal weight, overweight, and classes 1, 2, and 3 obesity. The results show significant differences (P-value<0.05) in prevalence by weight class for all but three of 82 diseases considered. The percentage of patients with multimorbidity (excluding obesity) increases from in 55.1% in patients with normal weight, to 57.88% with overweight, 70.39% with Class 1 obesity, 73.99% with Class 2 obesity, and 71.68% in Class 3 obesity, increasing most substantially with the progression from overweight to class 1 obesity. Most prevalent disease clusters expand from only hypertension and dorsalgia in normal weight, to add joint disorders in overweight, lipidemias in class 1 obesity, diabetes in class 2 obesity, and sleep disorders and chronic kidney disease in class 3 obesity. Recognition of multimorbidity patterns associated with weight increase is essential for true precision care of obesity-associated chronic conditions and can help clinicians identify and address preclinical disease before additional complications arise.

Effect of risk management combined with precision care in interventional embolization of cerebral aneurysm in elderly patients.

OBJECTIVE: To investigate the effect of risk management combined with intraoperative precision care on the efficacy and safety of interventional embolization therapy for elderly patients with cerebral aneurysms. METHODS: In this prospective randomized controlled study, we included 60 elderly patients with cerebral aneurysm treated with interventional embolization. The patients were randomly divided into an experiment group (n=30) and a control group (n=30). The control group received conventional care during the interventional procedure, while the experiment group received risk management combined with precision care. The outcome of the procedure, time to disappearance of clinical symptoms, length of hospitalization, incidence of complications, neurological function and quality of life before and 3 months after the procedure in both groups were assessed and compared. RESULTS: Compared with the control group, the experiment group had significantly less intraoperative bleeding, shorter operative time (all P<0.001), shorter time to disappearance of clinical symptoms and shorter hospitalization (all P<0.001), and a lower rate of surgical complications (P<0.05). Three months after the operation, the experiment group had better neurological function and quality of life, with significantly lower mRs scores (modified Rankin scale), NIHSS (National Institute of Health Stroke Scale) and higher SF-36 scores (MOS item short from health survey) than those of the control group (both P<0.001). CONCLUSION: Risk management combined with precision care can effectively improve the surgical safety of interventional embolization in elderly patients with cerebral aneurysm, reduce the incidence of surgical complications, and thus improve the prognosis.