Digital Twins for Patient Care via Knowledge Graphs and Closed-Form
  Continuous-Time Liquid Neural Networks
Logan Nye
Digital twin technology has is anticipated to transform healthcare, enabling
personalized medicines and support, earlier diagnoses, simulated treatment
outcomes, and optimized surgical plans. Digital twins are readily gaining
traction in industries like manufacturing, supply chain logistics, and civil
infrastructure. Not in patient care, however. The challenge of modeling complex
diseases with multimodal patient data and the computational complexities of
analyzing it have stifled digital twin adoption in the biomedical vertical.
Yet, these major obstacles can potentially be handled by approaching these
models in a different way. This paper proposes a novel framework for addressing
the barriers to clinical twin modeling created by computational costs and
modeling complexities. We propose structuring patient health data as a
knowledge graph and using closed-form continuous-time liquid neural networks,
for real-time analytics. By synthesizing multimodal patient data and leveraging
the flexibility and efficiency of closed form continuous time networks and
knowledge graph ontologies, our approach enables real time insights,
personalized medicine, early diagnosis and intervention, and optimal surgical
planning. This novel approach provides a comprehensive and adaptable view of
patient health along with real-time analytics, paving the way for digital twin
simulations and other anticipated benefits in healthcare.