Siemens Healthineers Ag

Siemens Healthcare Diagnostics Gmbh

Poikavila Ullaskrishnan

Puneet Sharma

Gianluca Paladini

Eli Gibson

Youngjin Yoo

Ingo Schmuecking

Ren-Yi Lo

Teodora-Vanessa Liliac

Pragneshkumar Patel

Ralf Krumtünger

Larisa Micu

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

Systems and methods facilitate privacy-preserving data curation in a federated learning system by transmitting a portion of a potential data sample to a remote location. The portion is inspected for quality to rule out data samples that do not satisfy data curation criteria. The remote examination focuses on checking the region of interest but maintains privacy as the examination is unable to parse any other identifiable subject information such as face, body shape etc. because pixels or voxels outside the portion are not included. The examination results are sent back to the collaborators so that inappropriate data samples can be excluded during federated learning rounds.

Privacy-preserving data curation for federated learning

Systems and methods for determining an evaluation of one or more patients is provided. User input for evaluating one or more patients is received. A commit bundle is retrieved from a commit database. An evaluation of the one or more patients is determined based on the user input using a medical ontology configured with the retrieved commit bundle. The medical ontology is separate from the commit database. Results of the evaluation of the one or more patients are output.

Scalable deployment of ontology-based decision trees for clinical decision support and automated clinical workflows

Growing community of inventors

Lebanon, NH, United States of America

Poikavila Ullaskrishnan