=Paper=
{{Paper
|id=Vol-1613/paper_7
|storemode=property
|title=Towards Integrated Information Extraction and Facetted Search Applications in Nephrology
|pdfUrl=https://ceur-ws.org/Vol-1613/paper_7.pdf
|volume=Vol-1613
|authors=Danilo Schmidt,Hans-Jürgen Profitlich,Daniel Sonntag
|dblpUrl=https://dblp.org/rec/conf/esws/SchmidtPS16
}}
==Towards Integrated Information Extraction and Facetted Search Applications in Nephrology==
https://ceur-ws.org/Vol-1613/paper_7.pdf
Towards Integrated Information Extraction and
Facetted Search Applications in Nephrology
Danilo Schmidt1 , Hans-Jürgen Profitlich2 , and Daniel Sonntag2
1
Nephrology Department
Charité - Universitätsmedizin Berlin
10117 Berlin, Germany
2
German Research Center for Artificial Intelligence (DFKI)
66123 Saarbrücken, Germany
Abstract. This work focusses on our first integration steps of complex
and partly unstructured medical data into a clinical research database.
Our main application is an integrated facetted search tool in nephrology
based on automatic information extraction results from textual docu-
ments. We describe the details of our technical architecture which is
based on open-source tools—to be replicated at other universities, re-
search institutes, or hospitals.
1 Introduction
As medical records may cover a very long history of diseases (up to 30 years) and
include a vast number of diagnoses, symptoms, results, medications, and labora-
tory values, we could highly benefit from advanced search capabilities in clinical
information systems to allow for the retrieval of relevant data. However, medical
information systems often suffer from good search capabilities for data which
has many unstructured text parts. Therefore, concepts to implement knowledge
based systems, based on textual information extraction in medicine, are in focus
of many recent research initiatives [11].
In this paper, we propose a three stage process: (1) offline textual informa-
tion extraction from medical records in transplant medicine; (2) the generation
of interesting facetted search capabilities on the results of the previous stage;
(3) the combination of the information extraction results with structured labo-
ratory values (ongoing work). Such a facetted search application uses techniques
for accessing information organised according to a facetted medical classification
system, allowing users to explore a collection of diagnoses, symptoms, results,
medications, and laboratory values by applying multiple filters. Thus, facetted
search allows clinicians to analyse complex data sets along a medical and cog-
nitive (reflective) chain of decision-making; in particular, facetted search appli-
cations allow physicians to identify groups of patients with similar attributes.
This can provide valuable decision support, when physicians are confronted with
situations where rare or complex diseases require a high degree of specialist
knowledge to filter and interpret (unstructured) medical data.
2 Background and Related Work
The facetted search application is based on the nephrology database TBase R .
The web-based electronic patient record TBase R has been implemented in a Ger-
man kidney transplantation programme as a cooperation between the Nephrol-
ogy of Charité Universitätsmedizin Berlin and the AI Lab of the Institute of
Computer Sciences of the Humboldt University of Berlin [3,10]. Currently, TBase R
automatically integrates essential laboratory data (9.9 million values), clinical
pharmacology (237.000 prescribed medications), diagnostic findings from radi-
ology, pathology and virology (146.000 findings), and administrative data from
the SAP-system of the Charité (70.000 diagnoses, 25.000 hospitalisations). Two
groups of use cases for the application of facetted search in the medical field,
and nephrology, can be identified: first, the use in clinical research, and second,
the implementation in the individual treatment as a decision support system in
the clinical routine.
Sacco [7] describes an approach of a guided interactive diagnostic system
based an dynamic taxonomies. Biron et al. [2] describe an information retrieval
system for computerised patient records We extend these approaches by a special
multi-facet functionality. Our approach shows the following main advantages:
– In our facetted search application, the user may remove any restriction he
or she may have made in previous steps. This allows for a much better
navigation through the search space where related systems only allow the
subsequent thinning [8, 9].
– The ranking of facet values by cardinality supports the survey of remaining
subsets.
– We base automatically generated facets (e.g., disease/symptom relationships
and negations) on multi-term extraction and relation extraction, by employ-
ing state-of-the-art, high-precision textual information extraction modules.
Only recently, new text mining approaches on Web-based medical literature
have been proposed. For extracting adverse drug events from text [6] or auto-
matic symptom extraction from texts on rare diseases [4], for example. However,
clinical information extraction from patient records is still underrepresented and
underdeveloped in clinical settings. Earlier work includes evaluating context fea-
tures for medical relation mining on medical abstracts; the identification of se-
mantic relations, such as substance A treats disease B, remains a non-trivial
task [13]. Recent work and comparative baseline experiments include temporal
information extraction [5]. A special trend becomes apparent, the need for ontol-
ogy modelling of medical terminology and corresponding information extraction
results [12]. Because of enormous annotation costs, mainly unsupervised methods
are being used [1]. In industry and in the context of reliable clinical relevance,
however, very detailed (and labor-intensive) supervised rule-based approaches
represent the state-of-the-art.3
3
Here, we use our research project partner’s solution (Averbis), which is based on
shallow text parsing, see https://averbis.com/en/research/
3 System Architecture
The annotated texts are transferred in XMI format4 and stored in a local
database at DFKI (see figure 1). Important components are the Solr search
platform, the information extraction module, and the facetted search and presen-
tation user interface modules. Solr5 is an open source enterprise search platform
Fig. 1. System Architecture of the Integrated Information Extraction and Facetted
Search Application
used in many large websites and applications and is one of the most popular en-
terprise search engines.6 Solr runs as a standalone full-text search server and uses
the Lucene Java search library at its core for full-text indexing and (facetted)
search. We chose the Solr system mainly because of some interesting features
like facetted navigation, a query language that supports structured and textual
search, the possibility for automatic result clustering based on Carrot27 , its scal-
ability and extensibility through plug-ins, and its various APIs for input (text,
xml, JSON, etc.) and output (JSON, XML, PHP, python, etc.).
The first step in our process is offline informative extraction. The text data
for our system originate from the TBase R database of Charité Berlin contain-
ing medical information about nephrology patients. In the first phase we only
4
http://www.omg.org/spec/XMI/
5
http://lucene.apache.org/solr/
6
http://db-engines.com/en/ranking/search+engine
7
http://project.carrot2.org/
used about 5000 unstructured, free texts (no meta data or structured data of
patients) of four types: ’Befunde” (findings), ”Untersuchungen” (visits), ”Ent-
lassungsbriefe” (clinical reports), and ”Verläufe” (progress reports). These free
texts are processed by the project partner Averbis, which anonymises the texts
and adds annotations based on several medical reference systems and dictionaries
(LOINC8 , ICD109 , ABDAMED10 ).
A software module extracts the relevant medical tags and features and stores
these in a database structure similar to the i2b2 star structure (in order to
simplify the updates of the target system i2b211 ). The user interface to search
and explore the annotated text database by using facets is built as a web ser-
vice based on the Solr extension ”solarium” for PHP systems.12 This extension
provides for an API to specify all parameters necessary to create complex Solr
requests. The presentation/validation user interface of the system (see figure 2)
consists of two parts: the upper part shows the original text with highlighted an-
notations, the lower part contains tabs listing the different relevant annotations.
Clicking on an item in the lower part scrolls the text above to the corresponding
Fig. 2. Presentation/Validation User Interface
8
https://loinc.org/
9
http://www.icd-code.de/
10
http://www.wuv-gmbh.de/abdata-pharma-daten-service/datenangebot/abdamed/
11
https://www.i2b2.org/about/intro.html
12
http://www.solarium-project.org/
position. The original XMI contents representing the complete original annota-
tion information is shown in a pop-up window when a highlighted annotation in
the text is clicked. Accordingly, this page serves two different purposes: (1) the
presentation of the original text snipped found by the facetted search, and (2)
the validation of the annotations.
4 Conclusion and Outlook
We demonstrated that new facetted search applications in the use case of trans-
plant medicine in nephrology, based on open-source software tools and exchange-
able information extraction modules, are feasible and a very suitable decision-
support tool for the doctor: this type of a knowledge based system provides
physicians with a practicable tool for the analysis of medical data and decision
support for cohort selection. We developed a user interface for facetted search
which is based on the Solr Engine. In the next project phase, we will extend
the capabilities of the facetted search application, mainly including the follow-
ing aspects: (1) integration of existing structural information about patients
and treatments which includes numerical values, in relation to laboratory values
or medications in particular; (2) extending the user interface by adding visual
search and presentation techniques like ”foamtree”13 to further facilitate the
users exploration of the search space; (3) the integration of facetted search into
special use cases moving towards individualised medicine [11].
Acknowledgements
This research is part of the project ”clinical data intelligence” (KDI) which is
founded by the Federal Ministry for Economic Affairs and Energy (BMWi).
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