As the Internet of Things is getting closer and closer to human beings, a huge amount of data related to human activities is becoming available. This data facilitates the development of advanced models of human characteristics, encompassing physiological, psychological, and pathological dimensions. One of the main challenges of modern medicine is the definition of Human Digital Twins as a way to predict the future evolutions of human beings, with the final aim to define, tune and monitor treatments and responses properly. Given the complexity of the problem, involving several aspects, there is a heterogeneous deployment of different approaches in Human Digital Twins definition, mainly based on Artificial Intelligence techniques. As medicine and psychology need trustworthiness in Artificial Intelligence-based applications, founding such Human Digital Twins on explainable and interpretable models is of paramount importance. This chapter introduces an approach to a unified vision for the construction of Human Digital Twins. To highlight their advantages in decision support, the proposed framework combines both model-based and data-driven techniques, leveraging the explainability of the former and the accuracy of the latter. Three case studies will be presented to demonstrate the reproducibility of the proposed approach in the healthcare domain.