An architecture of open-source tools to combine textual information extraction, faceted search and information visualisation

TL;DR

This paper introduces an open-source architecture integrating textual data extraction, faceted search, and visualization to support clinical decision-making, demonstrated through nephrology and mammography case studies.

Contribution

It presents a replicable technical architecture combining open-source tools for extracting, searching, and visualizing medical textual data in clinical research.

Findings

Architecture supports complex medical data integration

Facilitates clinician interaction with unstructured data

Effective in nephrology and mammography applications

Abstract

This article presents our steps to integrate complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated faceted search tool, accompanied by the visualisation of results of automatic information extraction from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals. Our exemplary use cases are nephrology and mammography. The software was first developed in the nephrology domain and then adapted to the mammography use case. We report on these case studies, illustrating how the application can be used by a clinician and which questions can be answered. We show that our architecture and the employed software modules are suitable for both areas of application with a limited amount of adaptations. For example, in nephrology we try to answer questions about the temporal characteristics of event sequences to gain significant insight from the data for cohort selection. We present a versatile time-line tool that enables the user to explore relations between a multitude of diagnosis and laboratory values.