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.

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.