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

arXiv: https://arxiv.org/abs/1810.12627 · HTML: https://mlsanity.com/paper/1810.12627/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data](https://mlsanity.com/paper/2304.02208/) (2023)
- [Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop](https://mlsanity.com/paper/2301.09908/) (2023)

## Abstract (arXiv)

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.