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.