One of the challenges in healthcare processes, especially those related to multi-morbid patients who sufer from multiple disorders simultaneously, is not connecting the disorders in patients to process events and not linking events' activities to globally accepted terminology. Addressing this challenge introduces a new entity to the clinical process. On the other hand, it facilitates that the process is interpretable and analyzable across diferent healthcare systems. This paper aims to introduce a tool named CEKG that uses event logs, diagnosis data, ICD-10, SNOMED-CT, and mapping functions to satisfy these challenges by constructing event graphs for multi-morbid patients' care pathways automatically.
all. Patients with multi-morbidity in particular need such care. This patient group, who have
multiple chronic conditions at the same time [
A clinical process or care pathway outlines the events involved in diagnosing, treating,
managing, and following up with patients [
• Connecting clinical entities. Emerging clinical entities are clinical attributes that are
not connected directly to events but can potentially be used as new entities. For example,
multi-morbid patient disorders are connected to the patient entity but not attached to
events. Multi-morbid patients have many diferent (sometimes emerging) disorders, which
we see as entities connected to the patient. By connecting events to the relevant disorders,
thereby getting a multi-entity event data, we can better query the event data of a patient
to find relevant insights[
In this paper we introduce a tool for the Clinical Event Knowledge Graph (CEKG) framework
presented in our previous work [
In Section 2, we describe the innovations of this tool and in Sect. 3 we show its application in a case study.
Since the tool needs to support terminologies such as SNOMED-CT and ICD-10 as parts of its
inputs a graph database was chosen for storage since it supports a linked-data structure for
these terminologies. Furthermore, the need for path-based traversal of data makes the graph
database an ideal choice. Additionally, the tool requires the storage of entity attributes and other
semantic patient data, further reinforcing the suitability of a graph database for these functions.
The CEKG was proposed using the Labelled Property Graph Model. However, a challenge in
creating a CEKG using Neo4J is integrating data from diferent sources (SNOMED, hospital
information systems, etc.), which requires using several complex Cypher Query Language (CQL)
queries manually. [
In addressing usability challenges, process mining tools frequently exhibit operational complexities, particularly when applied by healthcare professionals managing patients with multimorbid conditions. To address these dificulties, the CEKG tool incorporates a user interface that is designed, enabling users to rapidly assimilate its functionalities from the initial steps. Furthermore, the tool generates outputs in the LPG format and employs the Graphviz library for visualization purposes.
For the implementation of the tool, as illustrated in Fig. 1, Python with Django and Django Channels was used as the backend framework along with several libraries such as Pandas, Neo4j, and Graphviz. For frontend development, vanilla JavaScript and HTML and CSS were used.
The CEKG tool ofers several features for discovering various types of care pathways that integrate both connecting entities and linking terminology:
C1 Independent graphs for each patient without consolidating patient activities. C2 Combined graphs for patients without consolidating patient activities.
C3 Consolidated patient activities to identify repeated activities for patients with the same multi-morbidity or for specific disorders.
C4 Consolidated patient activities to determine how frequently activities related to the treatment of each disorder are repeated for a group of patients with the same multimorbidity.
C5 Consolidated Patient Care Pathways to identify the most frequently repeated activities in the treatment of a group of patients with the same multi-morbidity.
C6 Care Pathways that indicate which disorders are treated, untreated, or newly discovered in each admission.
Additionally, we can determine whether to include properties of activities in the graph. For example, should the graph only indicate that a specific clinical test, such as the ABG test, was conducted, or should it also include the test results (e.g., the values of Oxygen, Hemoglobin, ...). Furthermore, we can segment the graph by relating it to the domain or scope of activities.
The tool is designed to handle large datasets, with the primary dataset extracted from the entire MIMIC-IV database. However, it can also be used with alternative datasets. To facilitate testing and provide a template for users to create their own datasets, a test dataset was prepared by extracting a portion of the MIMIC-IV data. The activity titles and patient IDs were modified for de-identification. This test dataset includes essential ICD codes and SNOMED-CT IDs, although the tool is capable of processing the full range of ICD codes and SNOMED-CT ID databases. For convenience, all data in the test dataset was consolidated into a single spreadsheet. However, for practical use, separate CSV files can be imported into the tool.
For building the clinical event knowledge graph, diferent steps were defined:
At each step, Neo4j queries are automatically generated based on the input dataset to create graphs or establish relationships between two graphs. These queries will adapt if the dataset is changed. Some queries are designed to clear the database, remove or create constraints, create or modify nodes, or establish relationships between nodes. Since the tool sends the queries to the Neo4j Aura Database, it is possible to view the final clinical event knowledge graph within Neo4j Aura. However, the tool also facilitates the creation of the clinical event knowledge graph ofline in a local Neo4J instance by providing all the necessary queries for the user.
In this section, we validated the CEKG tool with a case based on the MIMIC-IV dataset [
The C2 care pathway discovered from the tool, as shown in Fig. 2, is the dependent care pathways of two multi-morbid patients, consisting of three entities: PATIENT with red circles, ADMISSION with blue circles, and Disorder with green circles. All activities in the process are mapped to concepts from SNOMED-CT. Additionally, the domains of activities are shown with diferent colors in the graph. With this type of care pathway, we can determine which activities that happened for these patients are related to which disorders. For example, the graph "Analysis of Arterial blood gases and pH" relates to two disorders with SNOMED-CT 1085006 and 94181007. Furthermore, we can categorize the activities using SNOMED-CT concepts.
The C3, as shown in Fig. 3, is the identification of the most frequent activities in the treatment of two patients. For example, we can find out how many times the "Microbiology Procedure" happened after the "Analysis of Arterial Blood Gases and pH" for these two patients. Using SNOMED-CT concepts as a label of activities facilitates the interpretation of the resulting care pathways universally across all health organizations.
To sum up, the tool streamlines the creation of standardized care pathways by integrating any event log with ICD codes and SNOMED CTs using a graph database. It also automates the Hospital admission 6 16 32 27 Ward 21 sBaslmeonoptdle from microbiology 223 Dhisocshpaitragle
to laboratory 2 2 4 2 2 6 4 4 8 2 generation and execution of the necessary queries for building the graph database, ensuring a seamless process. One area of future research could focus on identifying additional care pathways from the clinical event knowledge graph.