Digital Twins of Patients: A Cohort Matching Interpretation

Digital Twins (DTs) are essentially virtual replicas of physical entities. DTs have evolved significantly over time. They have been applied in various fields. Very recently, their application in the field of healthcare is also being explored. In healthcare, the creation of DTs of patients is of interest. DTs of patients show potential to perform as clinical decision support tools to enhance precision and personalization of treatment. An essential part of this role of DTs involves deriving sense from health and medical data. Personalization in this context requires looking at data of a present patient and identifying records of cohorts of past patients that are closely matching the present patient. Such matching cohorts allow for decision support on more personalized grounds. This paper presents an attempt to achieve such personalization through cohort matching. As part of an ongoing study, we do this to assist with immunotherapy treatment planning for triple-negative breast cancer as a case study.