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We introduce a simulated digital model that learns a person's optimal bloodhealth over time. Using a learning adaptive algorithm, our model provides arisk assessment score that compares an individual's chronological age frombirth to an estimation of a biological immune age derived from the score. Wedemonstrate its efficacy against real and synthetic data from medicallyrelevant cases, extreme cases, and empirical blood cell count data from 100Kdata records in the Centers for Disease Control and Prevention's NationalHealth and Nutrition Examination Survey (CDC NHANES) that spans 13 years. Wefind that the score is informative when distinguishing healthy individuals fromthose with diseases, both self-reported and abnormal blood tests manifested,providing an entry-level score for patient triaging. We show that, by analyzingan individual's Full Blood Count (FBC) or Complete Blood Count (CBC), testresults over time allows us to calculate an immune age score that correlateswith chronological age. The immune age score, derived solely from popularhematological markers, can be widely used to serve purposes of precisehealthcare, and predictive medicine.

An Adaptive Computational Intelligence Approach to Personalised Health Assessment and Immune Age Characterisation from Common Haematological Markers