Authors - Julian Andres Duarte Suarez, Leonardo Juan Ramirez Lopez Abstract - Given the continuous need of beds available for hospitalization in health institutions, and even more so during pandemic periods, it is necessary to have alternatives that allow patients to be transferred to their homes and from there to carry out continuous monitoring of their health. For this, DATALOG was developed, which is an Internet of Medical Things platform that acquires, processes, transmits, stores and manages the medical signals of patients from their home to a central hospital. Following this, the stored data is processed by applying the Standard Intersectoral Process methodology for data mining that al-lows them to provide the medical staff with the behavior of six physiological variables and visualize it in a unique control table developed in php. Initial tests show an acceptance of the medical staff as a service and support tool for medical decisions, and, in addition, it has been proven that hospitalization at home con-tributes significantly to the rapid improvement of the patient, thus decongesting the hospital system. Among the most recent innovations, DATALOG includes an early warning system that allows to warn about the patient's condition between normal, attention, alert and critical, with the possibility of sending the alert to mobile systems. It is concluded that DATALOG is a useful service and tool for e-health, and machine learning techniques allow for the prediction of patient states and alerts.
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