Modeling Clinical Data with Attention: A Knowledge Graph Approach with CliniKG

Given a set of historical, unlabeled patient records, how can we model the relationships between various concepts related to health conditions and treatments? Modeling health data as knowledge graphs can aid in better understanding and mining recurrent and abnormal patterns within massive amounts of records. However, for generic data modeling, the concepts and relationships of the data are often defined manually, which can be laborious and prone to human errors. The lack of structure in textual reports makes modeling challenging, as there is typically no standard in information, terminology, or other elements. This work proposes CliniKG for utilizing Large Language Models (LLMs) to structure patient records, enabling the modeling of health data concepts. First, we employ LLMs with zero-, or few-shot learning to define the graph's relationships of pairwise concepts. Based on the resulting modeling, we extract meaningful features from nodes and define their semantics automatically. With data visualizations, CliniKG highlights key findings, such as recurrent or rare relationships. The experimental evaluation shows CliniKG in action using a real dataset from a public hospital in Brazil. We performed a qualitative analysis with 40 domain experts to evaluate medium-sized LLMs and prompt con-figurations. The study reveals interesting patterns automatically identified within the data. CliniKG exhibits linear performance relative to the number of nodes, and the visual tools can assist specialists in monitoring patients' conditions.