3 research outputs found
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Reliable and resilient AI and IoT-based personalised healthcare services: A survey
Recent technological (e.g., IoT, 5G), and economic (e.g., UN 2030 Sustainable Development Goals) developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized through control and monitoring applications that are typically developed using artificial intelligence (AI)/machine learning (ML) based algorithms, that play a significant role to highlight the efficiency of traditional healthcare systems. Current personalized healthcare services are dedicated in a specific environment to support technological personalization (e.g., personalized gadgets/devices). However, they are unable to consider different inter-related health conditions, leading to inappropriate diagnosis and affect sustainability and the long-term health/life of patients. Towards this problem, the state-of-the-art Healthcare 5.0 technology has evolved that supersede previous healthcare technologies. The goal of healthcare 5.0 is to achieve a fully autonomous healthcare service, that takes into account the interdependent effect of different health conditions of a patient. This paper conducts a comprehensive survey on personalized healthcare services. In particular, we first present an overview of key requirements of comprehensive personalized healthcare services (CPHS) in modern healthcare Internet of Things (HIoT), including the definition of personalization and an example use case scenario as a representative for modern HIoT. Second, we explored a fundamental three-layer architecture for IoT-based healthcare systems using both AI and non-AI-based approaches, considering key requirements for CPHS followed by their strengths and weaknesses in the frame of personalized healthcare services. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions. Finally, we propose a methodology to develop reliable, resilient, and personalized healthcare services that address the identified weaknesses of existing approaches
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On the Validation of Multi-Level Personalised Health Condition Model
This paper presents a verification-based methodology to validate the model of personalised health conditions. The model identifies the values that may result in unsafe, unreachable, in-exhaustive, and overlapping states those can otherwise threaten the patient's life as a result of producing false alarms by accepting suspicious behaviour of the target health condition. Contemporary approaches to validating a model employ various testing, simulation and model checking techniques to recognise such vulnerabilities. However, these approaches are neither systematic nor exhaustive and thus fail to identify those false values or computations that estimate the health condition at run-time based on the sensor or input data received from various IoT medical devices. We have demonstrated our validation methodology by validating our example multi-level model that describes three different scenarios of Diabetes health conditions
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Towards Multi-Level Modelling and Monitoring of Real-time Personalised Health Conditions
This paper presents an example-based demonstration of our initial results on the modelling of health conditions to support the control and monitoring of (clinically) personalized healthcare services. The main goal of this work is to model health conditions at the interface, mechanical, biological, and environmental levels to support rigorous and reliable personalised healthcare services for unreliable IoT-based Healthcare 5.0 using different abstractions like rules, processes, and rates. Current approaches support either fine-grained (i.e., at DNA or protein level considering the effect of health conditions on cellular components, biological processes, and molecular functions) or coarse-grained (i.e., only parameters check ignoring the internal details and causes) modelling of various health conditions that hindered their usability to automatically control and monitor healthcare conditions in real-time due to lack of missing dependent information at different levels of health condition. Modelling health conditions is a challenging task because health condition is a complex process that involves various inter-dependent cyber, physical, mechanical, and biological sub-processes. We have developed a technique for modelling health conditions at multiple levels that supports various cyber, physical, and biological characteristics of health-condition and operate at different levels of abstraction including both coarse-grained and fine-grained levels that are related. We demonstrate the modelling technique through its application to a typical healthcare condition that includes Diabetes and Heart conditions