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The CEKG: A Tool for Constructing Event Graphs in the Care Pathways of Multi-Morbid Patients

Milad Naeimaei Aali, Felix Mannhardt, Pieter Jelle Toussaint

TL;DR

This paper aims to introduce a tool named CEKG that uses event logs, diagnosis data, ICD-10, SNOMED-CT, and mapping functions to satisfy challenges by constructing event graphs for multi-morbid patients' care pathways automatically.

Abstract

One of the challenges in healthcare processes, especially those related to multi-morbid patients who suffer 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 different 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.

The CEKG: A Tool for Constructing Event Graphs in the Care Pathways of Multi-Morbid Patients

TL;DR

This paper aims to introduce a tool named CEKG that uses event logs, diagnosis data, ICD-10, SNOMED-CT, and mapping functions to satisfy challenges by constructing event graphs for multi-morbid patients' care pathways automatically.

Abstract

One of the challenges in healthcare processes, especially those related to multi-morbid patients who suffer 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 different 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.

Paper Structure

This paper contains 3 sections, 3 figures.

Table of Contents

  1. Introduction
  2. The Overview of the Tool
  3. Use cases overview

Figures (3)

  • Figure 1: The tool was developed using Python with Django and Django Channels as the backend framework, supplemented by libraries including Pandas, Neo4j, Selenium, and Graphviz. The frontend was created using vanilla JavaScript, HTML, and CSS.
  • Figure 2: The resulting event graph includes disorder as a new entity. Activities and processes are categorized in a standardized manner, making them interpretable across different healthcare systems.
  • Figure 3: The resulting event graph identifies the most frequently repeated activities in treating two patients, all mapped to SNOMED-CT for standardized analysis.