Abstract: A method for predictive diagnosis of a disease in a person, the method includes (i) obtaining, by a machine learning process hosted by a first processing circuit, a health related data of the person that is stored in a first data structure; (ii) applying by the first processing circuit, the machine learning process, on the health related data to convert the health related data into a vector that provides a compact representation of the health related data, the vector comprises disease predicting information of the health related data; (iii) storing the vector in a second data structure; (iii) obtaining the vector by a classifier model hosted by a second processing circuit; (iv) applying, by the second processing circuit, the classifier model to the vector to identify whether there is a likelihood of the person having or developing the disease; and (v) storing an outcome of the applying of the classifier model in a third data structure; wherein the storing of the outcome makes available the outcome to one or m

Abstract: A method for reducing dependency of non-discrete laboratory measurements results on personal parameters, the method includes (i) storing, in a storage unit, health related events (HREs) count variables that represent an occurrence of HREs of different types in relation to a group of patients; (ii) granting, to a plurality of users, a remote access; (iii) converting the HREs count variables to normalized HRE information items; wherein a normalized HRE information item related to the HRE count variable is normalized to the patients of the group and is normalized to the HREs of the different types that are related to the patient; wherein the converting comprises applying a term frequency and inverse document frequency (TF-IDF) process; and (iv) storing the normalized HRE information items in the storage unit; wherein the storing of the normalized HRE information items make available to at least one user of the plurality of users the normalized HRE information items.