Preventative Value of Predictive Health Interventions on PICC-Related Thrombosis in Patients with Severe Burns

Background Burn mainly refers to the damage caused by high temperature to the skin and mucous membrane tissue. Seriously, it causes damage to the subcutaneous tissue or subcutaneous mucous membrane, which is easy to induce infection and poses a threat to the life safety of patients.For patients with deep burns, surgical removal of damaged skin and mucosal tissue, Postoperative,immediate coverage of surgical wounds,avoid infection due to body fluid and energy loss with related tissue necrosis.The clinical treatment period for critical burn patients is longer and high incidence of postoperative complications.The corresponding nursing intervention while undergoing treatment can help to eliminate the impact of the bad psychological state on the patients, improve the treatment compliance, and reduce the occurrence of complications. Objective Evaluation of the value of predictive health intervention in preventing thrombosis associated with Peripherally Inserted Central Catheter (PICC) via peripheral vein in critically ill burn patients Methods Select 90 patients with severe burn treated by PICC infusion from January 2019- February 2021, Group by reference to intervention methods, with 45 using conventional health intervention (control group) and another 45 using predictive health interventions (observation group). The incidence of pulmonary and wound infection, the incidence of adverse events associated with PICC catheterization and wound healing time were recorded in the two groups. The degree of pain was evaluated by visual simulation (VAS) score, and the differences of platelet, D- dimer (D-D) and hemodynamics were detected in the two groups Results The incidence of pulmonary infection, PICC associated thrombus and total adverse events in the observation group was lower, and higher rate of functional recovery,but less wound healing time with control group,which had statistical significance (P<0.05) .Incidence of wound infection, incidence of catheter blockage, incidence of unplanned extubation,the difference was not statistically significant (P>0.05) .Comparison before intervention/The pain score decreased at 7 ck 14d> 21d (P<0.05), and the observation group was lower than the control group (P<0.05) .Intervention of the 7d, 14d two sets all platelet elevation (P< 0.05) ,but D-D concentration decreases (P< 0.05) .Intervention of the 7d> 14d two sets was increased of the intrathoracic blood volume index (ITBVI) (P<0.05) , but extravascular lung water index (EVLWI) and mean arterial pressure (MAP) the difference was not statistically significant (P>0.05). Conclusions Predictive health intervention can reduce the pain degree of critically burned patients, reduce the incidence of adverse events such as infection and PICC-related thrombosis, and promote wound recovery.

Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health

In order to explore opportunities for personalized and predictive health care, we collected serial clinical measurements, health surveys and multiomics profiles (genomics, proteomics, autoantibodies, metabolomics and gut microbiome) from 96 individuals. The participants underwent data-driven health coaching over a 16-month period with continuous digital monitoring of activity and sleep. Multiomics factor analysis resulted in an unsupervised, data-driven and integrated view of human health, revealing distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet and hormonal effects. The data revealed novel and previously uncovered associations between risk factors, molecular pathways, and quantitative lifestyle parameters. For example, ethinyl estradiol use had a distinct impact on metabolites, proteins and physiology. Multidimensional molecular and digital health signatures uncovered biological variability between people and quantitative effects of lifestyle changes, hence illustrating the value of the combined use of molecular and digital monitoring of human health.

An Open Source Framework Approach to Support Condition Monitoring and Maintenance

This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Predictive Health Analytic Model in Federated Cloud

Predictive Health Analytic is a challenging discipline in healthcare industry where knowledge can be transferred into action. The basic steps in predictive modeling are to define the problem, gather the initial necessary data and evaluate several different algorithm approaches. Later his process to be refined by selecting best performing models, testing with bench mark data sets and real world setting. Predictive analytics helps to extract useful knowledge and support in making decisions. In this paper, federated health providers are interconnected by using brokers, gather information and helps in decision making related to the issues of health. Each provider has provided the awareness about the distinct diseases, predict the possible level of diseases affected and the mode of treatment. Simulation result reveals that the proposed architecture is essential for the present needs of human life.