Siemens Aktiengesellschaft

Zynx Health Incorporated

Mathaeus Dejori

Ciprian Dan Raileanu

Claus Neubauer

Vinay Damodar Shet

Nazif Cihan Tas

Frank Popp

Ekkehard Toensing

Ravi Kiran Reddy Palla

Dan G Tecuci

Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Clinical content analytics engine

A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.

Meta-data approach to querying multiple biomedical ontologies

Adaptive remote maintenance of rolling stocks is provided by machine-learning () of rules. Existing rules or models are automatically updated. Machine learning () is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction () by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning () of a new rule. The machine learning () uses user feedback to update the rule set.

Adaptive remote maintenance of rolling stocks

In a framework for acquiring and analyzing data from a network of sensors, plug-in software interfaces are used to provide scalability and flexibility. Data collection set-up data is exchanged through one or more first plug-in software interfaces with data collection devices, to configure the processor to collect measurement data from the data collection devices. Analysis set-up data is exchanged through one or more second plug-in software interfaces with one or more data analysis software packages, to configure the processor to provide a predefined subset of the measurement data to the data analysis software packages and to accept analysis results from the data analysis software packages. Measurement data and analysis results are subsequently exchanged through the plug-in interfaces.

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Los Angeles, CA, United States of America

Mathaeus Dejori