Extending the TOP Framework with an Ontology-Based Text Search Component.

INTRODUCTION: Constructing search queries that deal with complex concepts is a challenging task without proficiency in the underlying query language - which holds true for either structured or unstructured data. Medical data might encompass both types, with valuable information present in one type but not the other. METHOD: The TOP Framework provides clinical practitioners as well as researchers with a unified framework for querying diverse data types and, furthermore, facilitates an easier and intuitive approach. Additionally, it supports collaboration on query modeling and sharing. RESULTS: Having demonstrated its effectiveness with structured data, we introduce the integration of a component for unstructured data, specifically medical documents. CONCLUSION: Our proof-of-concept shows a query language agnostic framework to model search queries for unstructured and structured data.

Tracking Changes for Inter-Version Interoperability in Heterogeneous Evolving Medical Terminologies.

INTRODUCTION: Medical terminologies and code systems, which play a vital role in the health domain, are rarely static but undergo changes as knowledge and terminology evolves. This includes addition, deletion and relabeling of terms, and, if terms are organized hierarchically, changing their position. Tracking these changes may become important if one uses multiple versions of the same terminology and interoperability is desired. METHOD: We propose a new method for automatic change tracking between terminology versions. It consists of a declarative import pipeline, which translates source terminologies into a common data model. We then use semantic and lexical change detection algorithms. They produce an ontology-based representation of terminology changes, which can be queried using semantic query languages. RESULTS: The method proves accurate in detecting additions, deletions, relocations and renaming of terms. In cases where inter-version term mapping information is provided by the publisher, we were able to highly enhance the ability to differentiate between simple additions/deletions and refinements/consolidation of terms. CONCLUSION: The method proves effective for semi-automatic change handling if term refinements and consolidation are relevant and for automatic change detection if additional mapping information is available.

Modelling Adverse Events with the TOP Phenotyping Framework.

The detection and prevention of medication-related health risks, such as medication-associated adverse events (AEs), is a major challenge in patient care. A systematic review on the incidence and nature of in-hospital AEs found that 9.2% of hospitalised patients suffer an AE, and approximately 43% of these AEs are considered to be preventable. Adverse events can be identified using algorithms that operate on electronic medical records (EMRs) and research databases. Such algorithms normally consist of structured filter criteria and rules to identify individuals with certain phenotypic traits, thus are referred to as phenotype algorithms. Many attempts have been made to create tools that support the development of algorithms and their application to EMRs. However, there are still gaps in terms of functionalities of such tools, such as standardised representation of algorithms and complex Boolean and temporal logic. In this work, we focus on the AE delirium, an acute brain disorder affecting mental status and attention, thus not trivial to operationalise in EMR data. We use this AE as an example to demonstrate the modelling process in our ontology-based framework (TOP Framework) for modelling and executing phenotype algorithms. The resulting semantically modelled delirium phenotype algorithm is independent of data structure, query languages and other technical aspects, and can be run on a variety of source systems in different institutions.

Concept Graphs: A Novel Approach for Textual Analysis of Medical Documents.

The task of automatically analyzing the textual content of documents faces a number of challenges in general but even more so when dealing with the medical domain. Here, we can't normally rely on specifically pre-trained NLP models or even, due to data privacy reasons, (massive) amounts of training material to generate said models. We, therefore, propose a method that utilizes general-purpose basic text analysis components and state-of-the-art transformer models to represent a corpus of documents as multiple graphs, wherein important conceptually related phrases from documents constitute the nodes and their semantic relation form the edges. This method could serve as a basis for several explorative procedures and is able to draw on a plethora of publicly available resources. We test it by comparing the effectiveness of these so-called Concept Graphs with another recently suggested approach for a common use case in information retrieval, document clustering.