How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

A System for Rapid Configuration of Distributed Workflows over Web Services and their Handheld-Based Coordination

Web services technology has lately stirred tremendous interest in industry as well as the academia. Web services are self-contained, platform independent functionality which is available over the internet. Web services are defined, discovered & accessed using a standard protocols like WSDL, UDDI & SOAP. With the advent of Service-Oriented Architecture and need for more complex application, it became eminent to have a way in which these independent entities could collaborate in a coherent manner to provide a high level functionality. But the problem of service composition is not an easy one. One reason being the self-contained and loosely coupled interaction style, which happens to be the single most important reason for its popularity. We are proposing a prototype system for distributed coordination of web services. This system is based on the Web Bonds model for coordination. The system, dubbed BondFlow system, allows configuration and execution of workflows configured over web services. Presently BondFlow system allows both centralized as well as distributed coordination of workflows over handhelds, which we claim as an engineering feet and is currently a unique work in this area

SPHERICAL CRYSTALLISATION: A REVOLUTION IN THE FIELD OF PARTICLE ENGINEERING

Now-a-days direct tabletting technique is gaining more importance in Pharmaceutical manufacturing because it save money and time for tabletting but Good flowability and compressibility is prerequisite for drug to be prepared by direct compression. There are several techniques available to impart desired compressibility to drug, but the Spherical crystallization technique is the most promising one in which the drug crystals are modified using different solvents to directly compressible spherical agglomerates. Spherical agglomeration is particle engineering technique which involves the transformation of fine crystals into spherical shape which in turn enhances the powder properties such as particle size, shape, flow properties, solubility and bioavailability of pharmaceutical drug substances. The spherical crystallization further developed use with hydrophilic polymers to enhance dissolution rate characteristics of poorly water soluble drugs and can also be applied to sustain the drug release from solid dosage forms. The present review aims at the detailed comprehensive study about the technique, advantages and disadvantages, mechanism, different manufacturing methods of spherical agglomerates and characterization of spherical agglomerates

MetaverseKG: Knowledge Graph for Engineering and Design Application in Industrial Metaverse

While the term Metaverse was first coined by the author Neal Stephenson in 1992 in his science fiction novel “Snow Crash”, today the vision of an integrated virtual world is becoming a reality across different sectors. Applications in gaming and consumer products are gaining traction, industrial metaverse applications are, still in their early stages of development with one of the challenges being interoperability across various metaverse development platforms and existing software tools. In this work we propose the use of a knowledge graph based semantic data exchange layer, the Metaverse Knowledge Graph, to enable seamless transfer of information across platforms. We discuss how this approach addresses the challenge of interoperability and leads to better interactivity and synchronization across tools

An Ontology Design Pattern for Representing Causality

The causal pattern is a proposed ontology design pattern for representing the structure of causal relations in a knowledge graph. This pattern is grounded in the concepts defined and used by the CausalAI community i.e., Causal Bayesian Networks and do-calculus. Specifically, the pattern models three primary concepts: (1) causal relations, (2) causal event roles, and (3) causal effect weights. Two use cases involving a sprinkler system and asthma patients are provided along with their relevant competency questions

ACM web conference 2023

Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic explainability, new domain adaptability, and bias-free predictions that work with datasets of all sizes. In this tutorial of type lecture style, we detail how a richer representation of causality in AI systems using a knowledge graph (KG) based approach is needed for intervention and counterfactual reasoning (Figure 1), how do we get to model-based and domain explainability, how causal representations helps in web and health care

Informing healthcare operations with integrated pathology, clinical, and epidemiology data: Lessons from a single institution in Kenya during COVID-19 waves

Pathology, clinical care teams, and public health experts often operate in silos. We hypothesized that large data sets from laboratories when integrated with other healthcare data can provide evidence that can be used to optimize planning for healthcare needs, often driven by health-seeking or delivery behavior. From the hospital information system, we extracted raw data from tests performed from 2019 to 2021, prescription drug usage, and admission patterns from pharmacy and nursing departments during the COVID-19 pandemic in Kenya (March 2020 to December 2021). Proportions and rates were calculated. Regression models were created, and a t-test for differences between means was applied for monthly or yearly clustered data compared to pre-COVID-19 data. Tests for malaria parasite, Mycobacterium tuberculosis, rifampicin resistance, blood group, blood count, and histology showed a statistically significant decrease in 2020, followed by a partial recovery in 2021. This pattern was attributed to restrictions implemented to control the spread of COVID-19. On the contrary, D-dimer, fibrinogen, CRP, and HbA1c showed a statistically significant increase (p-value \u3c0.001). This pattern was attributed to increased utilization related to the clinical management of COVID-19. Prescription drug utilization revealed a non-linear relationship to the COVID-19 positivity rate. The results from this study reveal the expected scenario in the event of similar outbreaks. They also reveal the need for increased efforts at diabetes and cancer screening, follow-up of HIV, and tuberculosis patients. To realize a broader healthcare impact, pathology departments in Africa should invest in integrated data analytics, for non-communicable diseases as well

Personalized Digital Phenotype Score, Healthcare Management and Intervention Strategies Using Knowledge Enabled Digital Health Framework for Pediatric Asthma

Asthma is a personalized, and multi-trigger respiratory condition which requires continuous monitoring and management of symptoms and medication adherence. We developed kHealth: Knowledge-enabled Digital Healthcare Framework to monitor and manage the asthma symptoms, medication adherence, lung function, daily activity, sleep quality, indoor, and outdoor environmental triggers of pediatric asthma patients. The kHealth framework collects up to 1852 data points per patient per day. It is practically impossible for the clinicians, parents, and the patient to analyze this vast amount of multimodal data collected from the kHealth framework. In this chapter, we describe the personalized scores, clinically relevant asthma categorization using digital phenotype score, actionable insights, and potential intervention strategies for better pediatric asthma management

SUSTAINED RELEASE MATRIX TYPE DRUG DELIERY SYSTEM: A REVIEW

Oral sustained release (SR) products provide an advantage over conventional dosage forms by optimizing bio-pharmaceutics, pharmacokinetics and Pharmacodynamic properties of drugs in such a way that it reduce dosing frequency to an extent that once daily dose is sufficient for penetration, polymer swelling, drug dissolution, drug diffusion and matrix erosion. Highly water soluble drugs like Diltiazem; Ranitidine has been formulated as sustained release matrix tablets. This article contains the basic information regarding design sustained release formulation and also the different types of the same. Developing oral sustained release matrix tablet with constant release rate has always been a challenge to the pharmaceutical technologist. Most of drugs, if not formulated properly, may readily release the drug at a faster rate, and are likely to produce toxic concentration of the drug on oral administration. Hydrophilic polymers have become product of choice as an important ingredient for formulating sustained release formulations.Key Words: Matrix system, Controlled drug delivery, Polymers.Â