The progress of the Internet of Medical Things (IoMT) and mobile technologies is a crucial driver for the evolution of healthcare systems in the path of prevention, early diagnosis and care, consequently unleashing the full potential of medical devices. Especially in intensive care, several vital signs can be monitored to provide an Early Warning Score (EWS) useful to detect the onset of pathological events or severe conditions. However, under these conditions, it would be beneficial to design a system that can provide a risk assessment even in the presence of a reduced number of vital signs. In this work, we propose an on-edge system, connected to one or more wearable medical devices, that is able to collect, analyze and interpret real-time clinical parameters and to provide an EWS-like clinical risk measurement. The system shows an evolutionary behavior by dividing the learning problem in two simpler ones, in order to correctly distinguish between low-urgency and emergency scenarios, with the possibility of selecting the most convenient configuration able to choose the most appropriate classifier even when the feature set does not allow a robust model selection. In particular, we focus on a comparative analysis of machine learning (ML) methods in different conditions of available vital parameter sets, evolving therefore to an adaptive ML approach. Moreover, since from the integration of artificial intelligence tools and IoMT, emerging ethical issues may arise about lack of transparency, a semantic-based explanation is associated to enrich the predictions along with the health data by means of Semantic Web technologies.

Evolving and explainable clinical risk assessment at the edge

Corrado Fasciano;
2022

Abstract

The progress of the Internet of Medical Things (IoMT) and mobile technologies is a crucial driver for the evolution of healthcare systems in the path of prevention, early diagnosis and care, consequently unleashing the full potential of medical devices. Especially in intensive care, several vital signs can be monitored to provide an Early Warning Score (EWS) useful to detect the onset of pathological events or severe conditions. However, under these conditions, it would be beneficial to design a system that can provide a risk assessment even in the presence of a reduced number of vital signs. In this work, we propose an on-edge system, connected to one or more wearable medical devices, that is able to collect, analyze and interpret real-time clinical parameters and to provide an EWS-like clinical risk measurement. The system shows an evolutionary behavior by dividing the learning problem in two simpler ones, in order to correctly distinguish between low-urgency and emergency scenarios, with the possibility of selecting the most convenient configuration able to choose the most appropriate classifier even when the feature set does not allow a robust model selection. In particular, we focus on a comparative analysis of machine learning (ML) methods in different conditions of available vital parameter sets, evolving therefore to an adaptive ML approach. Moreover, since from the integration of artificial intelligence tools and IoMT, emerging ethical issues may arise about lack of transparency, a semantic-based explanation is associated to enrich the predictions along with the health data by means of Semantic Web technologies.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/240760
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