Siemens Healthcare Gmbh

Siemens Healthcare Diagnostics Gmbh

Eugen Kubala

Dominik Neumann

Oliver Frings

Carsten Dietrich

Maximilian Würstle

Maximilian Weiss

Maximilian Wuerstle

A system is for infectious disease notification. The system includes at least one processor, configured to use a machine learning monitoring algorithm, trained on a large number of EMR datasets of patients, to calculate a probability for an infectious disease from a provided EMR dataset and compare the probability of the provided EMR dataset calculated with a known value. In training of the monitoring algorithm, the value represents whether there was an onset of an infectious disease or not and the monitoring algorithm is designed to adjust parameters of the monitoring algorithm. And in evaluating a notification, the value is a threshold value and the system is designed to output a notification upon the probability being greater than the threshold value.

System and method for infectious disease notification

A method and a device are for exchanging information regarding the clinical implications genomic variations. In an embodiment, the method includes receiving login-data of a user; evaluating the login-data received; establishing an encrypted data connection to the user after the evaluating indicates a positive evaluation of the login-data; saving, upon receiving a dataset in a context of a genomic variation, the dataset received in a memory, context-related with the genomic variation; and evaluating, upon a user request being received and connected with a search query for the genomic variation, a set of datasets from the memory, the datasets being context-related with the genomic variation and the set including the datasets that the user is authorized to receive, and sending the set of datasets to the user.

Method and device for exchanging information regarding the clinical implications of genomic variations

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Erlangen, Germany

Eugen Kubala