Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs
When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact that this prior knowledge is virtually impossible to reuse as-is. This is true also in domains, e.g., eHealth, where a lot of effort has been put into developing high-quality standards and reference ontologies, e.g. FHIR1. In this paper, we propose an integrated methodology, called iTelos, which enables data and knowledge reuse towards the construction of Interoperable Electronic Health Records (iEHR). The key intuition is that the data level and the schema level of an application should be developed independently, thus allowing for maximum flexibility in the reuse of the prior knowledge, but under the overall guidance of the needs to be satisfied, formalized as competence queries. This intuition is implemented by codifying all the requirements, including those concerning reuse, as part of a purpose defined a priori, which is then used to drive a middle-out development process where the application schema and data are continuously aligned. The proposed methodology is validated through its application to a large-scale case study.
Code (0)
등록된 구현이 없습니다.
Tasks
Knowledge GraphsSimilar Papers 제목 키워드 기반
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records
The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research into antimicrobial resistance (AMR). ARMD encompasses data from adu…
Causal InferenceDecision MakingDe-identificationDevelopment of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records
Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. One such type of information is medications which are not…
Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR)
Fast Healthcare Interoperability Resources (FHIR) is the dominant standard for interoperable exchange of healthcare data. In FHIR, electronic health records form a directed graph of resources. Answering clinically meanin…
Reinforcement LearningQuestion AnsweringCEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records
Electronic Health Records (EHRs) provide a rich, longitudinal view of patient health and hold significant potential for advancing clinical decision support, risk prediction, and data-driven healthcare research. However, …
Synthetic Data GenerationMedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realistic, synthetic electronic health records (…
Denoising