Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Corporation

Ren-Yi Lo

Sasa Grbic

George Lo

Livio Dalloro

Thomas Gruenewald

Peter Kob

Andreas Scholz

Christian Winkler

Poikavila Ullaskrishnan

Mina Azib

Hermann Friedrich

Arnaud Gaillard-Moguilny

For anonym izing or other keyword identification medical patient data, a conditional random field sequence classifier is used for the NER model for NLP, providing a technical solution to help the computer perform better at identifying PHI from context and reduce manual anonym ization efforts of medical reports. One tool or executable integrates report format conversion, annotation, training, and application. These operations may be selected, or the tool configured for anonymization or keyword identification. Different files from each stage may be exported or used by others operating on other computers, allowing collaboration or sequential burden sharing for anonym ization.

Anonymization of heterogenous clinical reports

A control layer automation device comprises a processor, one or more control layer applications, a database, a wireless interface, a device memory. Each control layer application is configured to perform a discrete set of automation functions. The database comprises a plurality of operator device identifiers and the wireless interface allows the one or more control layer applications to communicate with a plurality of operator devices via the plurality of operator device identifiers. The device memory comprises the one or more control layer applications. The control layer application manager is configured to manage execution of the one or more control layer applications on the processor.

Provisioning of control layer applications for use in industrial control environments

A computer-implemented method for automatically generating metadata tags for a medical image includes receiving a medical image and automatically identifying a set of body landmarks in the medical image using one or more machine learning models. A set of rules are applied to the set of body landmarks to identify anatomical objects present in the image. As an alternative to using the set of rules, in some embodiments, one or more machine learning models to the set of body landmarks to identify anatomical objects present in the image. Once the anatomical objects are identified, metadata tags corresponding to the anatomical objects are generated and stored in the medical image. Then, the medical image with the metadata tags is transferred to a data repository.

Detecting and classifying medical images based on continuously-learning whole body landmarks detections

An automation management system is deployed and operates on a plurality of network devices in a network in a distributed manner. The plurality of network devices may comprise a plurality of routers, for example. Automation management services are deployed and operate on the network devices in accordance with a service-oriented architecture model. At least one of the services may comprise a first instance of the service deployed on a first network device and a second instance of the service deployed on a second network device. Methods for deploying and operating an automation management system on a plurality of network devices in a network are also provided.

Method, system and apparatus for providing automation management services

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Plainsboro, NJ, United States of America

Ren-Yi Lo