Abstract: A method for early diagnosis of an autoimmune or chronic disease in subject. The method includes (i) selecting, out of missing existing health related data of the subject (HRDS) items, a subject-specific subset; (ii) obtaining at least one missing existing HRDS item; (iii) adding the at least one obtained HRDS item to an existing HRDS to provide an updated HRDS; (iv) applying to the updated HRDS, a second machine learning model adapted to convert parameters of the updated HRDS, some of which may be indicative of the early development stages of the disease, into a second vector that provides a compact representation of the updated HRDS that reflects on the medical condition of the subject; and (v) applying a second classifier model to the second vector to provide a second classification result that is indicative of a second likelihood of the subject having or developing the disease.

Abstract: Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of autoimmune conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed autoimmune conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patients medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subjects data to generate a patient diagnosis probability vector, predicting the existence of autoimmune conditions.

Abstract: Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of chronic medical conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed chronic conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patient's medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subject's data to generate a patient diagnosis probability vector, predicting the existence of chronic conditions.

Abstract: According to various embodiments, a computer-implemented method is disclosed that includes receiving, at a wireless adaptor of a device, a wireless data packet from an access point (AP), wherein the wireless data packet includes a Basic Service Set Identifier (BSSID) of the AP; changing the BSSID of the received data packet by a processor or hardware to produce a modified wireless data packet; and transmitting the modified wireless data packet to an application on the device.

Abstract: According to various embodiments, a computer-implemented method is disclosed that includes receiving, at a wireless adaptor of a device, a wireless data packet from an access point (AP), wherein the wireless data packet includes a Basic Service Set Identifier (BSSID) of the AP; changing the BSSID of the received data packet by a processor or hardware to produce a modified wireless data packet; and transmitting the modified wireless data packet to an application on the device.

Abstract: According to various embodiments, a computer-implemented method is disclosed that includes receiving, at a wireless adaptor of a device, a wireless data packet from an access point (AP), wherein the wireless data packet includes a Basic Service Set Identifier (BSSID) of the AP; changing the BSSID of the received data packet by a processor or hardware to produce a modified wireless data packet; and transmitting the modified wireless data packet to an application on the device.

Abstract: According to various embodiments, a computer-implemented method is disclosed that includes receiving, at a wireless adaptor of a device, a wireless data packet from an access point (AP), wherein the wireless data packet includes a Basic Service Set Identifier (BSSID) of the AP; changing the BSSID of the received data packet by a processor or hardware to produce a modified wireless data packet; and transmitting the modified wireless data packet to an application on the device.