Neuro-Symbolic Artificial Intelligence for Patient Monitoring

TL;DR

This paper advocates for using Neuro-Symbolic AI in patient monitoring, highlighting its potential to address technical challenges and improve human activity recognition in healthcare settings.

Contribution

It introduces a process architecture for NeSy-AI in patient monitoring and discusses its advantages over traditional methods.

Findings

NeSy-AI can enhance accuracy in patient activity recognition.

It offers solutions to technical challenges in healthcare monitoring.

The proposed architecture supports effective integration of symbolic and neural methods.

Abstract

In this paper we argue that Neuro-Symbolic AI (NeSy-AI) should be applied for patient monitoring. In this context, we introduce patient monitoring as a special case of Human Activity Recognition and derive concrete requirements for this application area. We then present a process architecture and discuss why NeSy-AI should be applied for patient monitoring. To further support our argumentation, we show how NeSy-AI can help to overcome certain technical challenges that arise from this application area.