Explainable Digital Twins of Patients: Towards Precision and Personalisation through Cohort Matching

Abstract

Modern healthcare services have advanced greatly due to rapid improvements in technology. The next generation of advancements requires precise and personalised treatments, especially for chronic diseases. Computational means are an effective way to achieve this through intelligent decision support assisted by superior data collection and analytics. An emerging concept to facilitate this is digital twins (DTs)—digital replicas of physical entities. DTs have evolved over the years across various industries including aerospace, control engineering, manufacturing, design optimization, and more. DTs in healthcare though, have been explored only relatively recently. One of the most interesting questions lies in creating DTs of humans to model healthcare aspects to enable intelligent decision support. Working towards this quest, this paper attempts to answer the research question: How might precise and personalised treatments for chronic diseases be planned in real-time through explainable digital twins? We attempt to answer this question in the context of breast cancer.

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