Integrating FHIR and UMLS in an Intelligent Tutoring System.

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.

InterAgent on FHIR - Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR.

INTRODUCTION: For an interoperable Intelligent Tutoring System (ITS), we used resources from Fast Healthcare Interoperability Resources (FHIR) and mapped learning content with Unified Medical Language System (UMLS) codes to enhance healthcare education. This study addresses the need to enhance the interoperability and effectiveness of ITS in healthcare education. STATE OF THE ART: The current state of the art in ITS involves advanced personalized learning and adaptability techniques, integrating technologies such as machine learning to personalize the learning experience and to create systems that dynamically respond to individual learner needs. However, existing ITS architectures face challenges related to interoperability and integration with healthcare systems. CONCEPT: Our system maps learning content with UMLS codes, each scored for similarity, ensuring consistency and extensibility. FHIR is used to standardize the exchange of medical information and learning content. IMPLEMENTATION: Implemented as a microservice architecture, the system uses a recommender to request FHIR resources, provide questions, and measure learner progress. LESSONS LEARNED: Using international standards, our ITS ensures reproducibility and extensibility, enhancing interoperability and integration with existing platforms.

A Secure Multi-Party Computation Protocol for Time-To-Event Analyses.

The cryptographic method Secure Multi-Party Computation (SMPC) could facilitate data sharing between health institutions by making it possible to perform analyses on a "virtual data pool", providing an integrated view of data that is actually distributed - without any of the participants having to disclose their private data. One drawback of SMPC is that specific cryptographic protocols have to be developed for every type of analysis that is to be performed. Moreover, these protocols have to be optimized to provide acceptable execution times. As a first step towards a library of efficient implementations of common methods in health data sciences, we present a novel protocol for efficient time-to-event analysis. Our implementation utilizes a common technique called garbled circuits and was implemented using a widespread SMPC programming framework. We further describe optimizations that we have developed to reduce the execution times of our protocol. We experimentally evaluated our solution by computing Kaplan-Meier estimators over a vertically distributed dataset while measuring performance. By comparing the SMPC results with a conventional analysis on pooled data, we show that our approach is practical and scalable.