Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques of facilitating processing of at least one DICOM SC image—e.g., using a PC or workstation in a hospital or an institution—to automatically extract clinical data therein are provided. Characters associated with the clinical data are extracted from the at least one DICOM SC image based on configuration information associated with the at least one DICOM SC image, which configuration information is obtained based on the at least one DICOM SC image.

Abstract: 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.

Abstract: Systems and Methods for adaptive aggregation in a federated learning model. An aggregation server sends global model weights to all chosen collaborators for initialization. Each collaborator updates the model weights for certain epochs and then sends the updated model weights back to the aggregation server. The aggregation server adaptively aggregates the updated model weights using at least a computed model divergence value and sends the aggregated model weight to collaborators.

Abstract: A medical knowledge base in a digital, clinical system is upgraded. A storage with a knowledge base, being a SNOMED knowledge base, is provided in a web ontology format. Procedural data, representing clinical procedures for evaluation of a patient's health state, is received. The received procedural data is mapped in a set of SNOMED expressions. The SNOMED expressions are converted into statements in the web ontology format. The SNOMED knowledge base is upgraded with the received procedural data by adding the statements in the SNOMED knowledge base for providing a processable file with an upgraded version of the SNOMED knowledge base.

Abstract: 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.