After more than 10 years in a consultancy for the life sciences, I joined Averbis in 2018 as Project Manager Healthcare. From the very beginning, I was fascinated by the fact that Averbis has retained the “start-up mentality”, but can draw on the experience of more than a decade of working with hospitals and service providers in the healthcare sector.

Together with our clients and partners, developing the potentials of text mining & AI for the health care sector is an exciting task and multifaceted, because our use cases range from medical research to the optimization of administrative processes.  Where else are informed decisions and the best possible use of resources as important as in healthcare? It makes me proud that I can make a contribution through my work at Averbis.

To apply new technologies in the medical environment with high-quality standards and data protection requirements is challenging.  The competence mix in the Averbis team helps us to address the complex needs of our clients. I like how openly and in partnership we act with our clients. And it is particularly important to me that I always have fellow teammates on my side at Averbis who are prepared to go an extra mile for excellent results!

Sonja Fix
Project Manager Healthcare

As Head of Platform, Christian Gaege is responsible for the Averbis text analytics and machine learning platform Information Discovery.

Christian, can you explain briefly what exactly Information Discovery is?
Information Discovery is the leading application platform for natural language processing (NLP) for the life science industry.
Information Discovery combines state of the art machine learning, terminologies and a powerful rule engine to uncover facts and relations in unstructured text data. It includes a large variety of components to identify document language, entities like companies and persons, part of speech, abbreviations, measurements, temporal expressions, keywords and negations. Domain specific components (to detect information like diagnoses, medications and laboratory values for health care scenarios) are available as add-ons.
Data scientists and software developers can easily extend Information Discovery to tailor the text analysis functionality to their specific needs. For example, we’ve recently achieved an accuracy of over 98 % within just a few hours of optimization for a specific customer scenario.

What is different to other tools in this area?
As we all know, more and more data is available in unstructured form (e.g. chat messages and social media posts, but also office documents, scientific papers, patents and patient records) but it’s hard to leverage this data.
Information Discovery helps you to exploit this potential for your business:
• BI on your unstructured data – extract facts from your documents and apply queries and analytics to get insights.
• Document classification – train your own machine learning model to automatically classify documents in any categories such as relevant or irrelevant. No machine learning experience is required.
• Multilingual support
• On premise installation or private cloud to protect sensitive data.
• Seamless integration into existing workflows or other applications through extensive API.
• No vendor lock-in due to open standards.

What benefits do I gain?
Gain competitive advantages from insights that were previously not accessible due to lack of structured information.
• Reduce development time by using Information Discovery as an off-the-shelf solution for natural language processing in your own products.
• Save money and reduce time spent for manual processes like document pre-classification.
• Benefit from Averbis’ many years of experience in the life science environment
• Be able to handle the increasing amount of unstructured data.

What fascinates you personally the most about this tool, Christian?
The wide range of use cases in which our customers use Information Discovery. For example, the software is currently used in the health sector to identify patients with a positive COVID-19 diagnosis and distinguish them from corona suspect cases.

Last but not least a personal question to get an insight info: work hard – play hard. Tell us about your favourite play time activity after work.
As a balance to work I like to do sports and spend time with my 3 children. Besides, I love music and try to improve my guitar playing.

Thank you for these valuable insights, Christian.

We also would like to point out: Demo version is available, if you got curious enough and would like to check it out by yourself.
Simply contact us!  www.averbis.com

Our partner TriNetX, through its global network of 150 healthcare organizations, is providing Covid-19 researchers with up-to-date patient-level clinical data for those diagnosed with the virus to help develop supportive, curative, and preventative therapies for the disease.

Crucial types of information from Covid-19 patients are currently only available in an unstructured form such as doctors’ letters, progress reports, radiology reports, etc. In the current situation COVID-19 is very often mentioned in these documents (e.g. patient is afraid of having Covid-19 or the patient’s wife has returned from a corona risk area). In order to improve the detection of positive COVID-19 diagnoses, Averbis and the technical team of TriNetX have jointly optimized the NLP based diagnosis detection for COVID-19.

Now it is possible to incorporate facts from unstructured documents to develop a study a cohort of COVID-19 patients.

Thank you for delivering so fast!

(Photo by Drew Hays unsplashed)

Abstract

Background and aims:Diagnosis of rare neurogenetic disorders is often challenging, particularly adult-onset presentations, with long diagnostic delays and misdiagnosis. As therapies become available, it is increasingly important to identify patients with rare neurologic diseases.

Methods:This multicenter project on ten rare neurogenetic diseases was approved by local Ethics committees and data protection authorities of six German University medical centers. Semantic text mining software structures medical data by ranking documents according to probability of disease, based on disease-specific lists of weighted signs and symptoms. Software and search algorithms were optimised in a pilot phase. Existing electronic medical records from the Department of Neurology of each center, corresponding to 10 years of activity, were screened, and patients ranked by probability of having the respective disease. An experienced team of physicians reviewed the data for the top ranked patients and those without a confirmed diagnosis were contacted for testing for the respective disease.

Results:In the pilot phase, 4 patients with Pompe disease and 4 heterozygous NPC1 mutation carriers were identified in Munich. More than 400.000 datasets from four centers were analysed for three diseases: Niemann-Pick type C disease, Pompe disease and Fabry disease. Four novel Pompe patients and 3 heterozygous NPC1or NPC2 mutation carriers were identified, who had not previously been diagnosed. Data from more centers will be provided.

Conclusion:Electronic medical records-based diagnostic data mining seems to be a promising tool to help diagnosing rare neurologic diseases. It may allow effective screening, re-evaluation of patients with uncertain diagnosis, and identification of patients for clinical trials.

Disclosure:
This research was supported by research grants from Sanofi Genzyme and Actelion.

Cite this article as:

Catarino C, Grandjean A, Doss S, Mücke M, Tunc S, Schmidt K, Schmidt J, Young P, Bäumer T, Kornblum C, Endres M, Daumke P, Klopstock T, Schoser B. mineRARE: Semantic text-mining of electronic medical records as diagnostic decision support tool to search for rare neurologic diseases such as Pompe disease, Fabry disease and Niemann-Pick type C disease. European Journal of Neurology 07/2017; 24(Suppl 1):75-75.

Information Discovery is a next-generation text analytics platform that allows you to gain insights into your unstructured data and explore key information in the most flexible way possible. Information Discovery collects and analyzes all kind of documents such as patents, research literature, databases, websites and company- internal repositories.

In recent months, Averbis has published versions 4.9 to 4.11 with a variety of improvements and extensions within a very short time. Highlights include the new modules of Terminology Editor for the maintenance of terminology and Annotation Editor for visualization of text mining results, as well as the horizontal scalability of text mining pipelines for a variety of big data scenarios.

Here are the most important innovations in versions 4.9 to 4.11 at a glance:

• With the newly developed Terminology Editor, terminology can now be edited directly in Information Discovery and seamlessly integrated in text mining pipelines.
• Text analysis pipelines can now be distributed horizontally on any number of machines. The distribution can be fully controlled via the graphical administration within Information Discovery.
• The new Annotation Editor lets you graphically visualize and edit text analysis results.
• The Evaluations Workbench allows the comparison of different text mining pipelines regarding precision, recall, F1, and standard deviation.

Information Discovery is a next-generation text analytics platform that allows you to gain insights into your unstructured data and explore key information in the most flexible way possible. Information Discovery collects and analyzes all kind of documents such as patents, research literature, databases, websites and company- internal repositories.

With the new version, we have revised our Information Discovery platform and significantly improved the interfaces and the configuration options.

The key new features of Version 4.8.0 include:

• New! Graphic configuration and administration of text analysis pipelines
• New! Monitoring of throughput numbers at the pipeline level and component level
• New! Restful text mining web services per Swagger framework
• New! Additional connectors to file systems and databases
• New! Numerous improvements for scalable and shared applications
• Various bug fixes

Individualized mathematical/systems medical models for the development of disease processes have the potential to predict future health events and individual therapy outcomes. Such predictive models can assist clinicians in diagnosing and treating their patients, and help patients to better understand their diseases. In order to develop a mathematical prediction model, a large range of varied clinical patient data must be gathered from information systems, curated, and extensively analyzed. Privacy must be ensured when dealing with personal and sensitive patient information. The complex predictive models obtained from analysis of the data must first be checked in elaborate clinical trials in terms of their prediction accuracy before they can be used in practice. Previously, this has been achieved by only a few models.

The BMBF project “XplOit” will facilitate and accelerate the complex process of preparing and combining clinical data, of model development and validation, as well for making it available for clinical use. This should be possible with a new generation of advanced so-called semantic data integration and information extraction tools, which are integrated with a modeling workbench in an IT platform.

© Fraunhofer IBMT, MEV Verlag

The “XplOit” platform is initially designed for the development and validation of predictive models to improve treatment after stem cell transplantation. The transplantation of hematopoietic stem cells from donors, for example, is used for the treatment of various forms of leukemia. Life-threatening complications can occur, such as viral infections or graft-versus-host reactions. Even disease relapses are often seen. At present, it is not yet possible to predict with high accuracy in which patients these complications will occur, and life-saving measures are often introduced too late. Using the “XplOit” platform, precise forecast models will be developed individually for each patient to predict possible complications, and to prevent them through timely clinical intervention or counteracting them at an early stage.

Launched in March 2016 and designed to run for 5 years, the collaborative project “XplOit” is implemented by an experienced international, multidisciplinary team of experts from the fields of medicine, systems biology, computational linguistics, and medical and bioinformatics. It is coordinated by the Fraunhofer Institute for Biomedical Engineering IBMT, which also holds the lead in development of the “XplOit” platform, and contributes core components for extracting information, integration, and analysis. The Institute for Formal Ontology and Medical Information Science at the University of Saarland is mainly responsible for the so-called Semantic Integration Framework platform. The company Averbis develops tools for the extraction of information from clinical text documents. Computer scientists from the Department of Pediatric Oncology and Hematology of the University of Saarland are in charge of data protection and develop anonymizing tools and parts of the modeling workbench system.

The modeling itself is done by the Max Planck Institute for Computer Science and the Department of Clinical Pharmacy of the University of Saarland. Clinical expertise and data are represented by the Department of Internal Medicine I — oncology, hematology, clinical immunology, rheumatology and the Institute of Virology at the University of Saarland, as well as by the Department of Bone Marrow Transplantation and the Institute of Virology at the University Hospital Essen. The clinical partners will validate the predictive models for stem cell transplantation developed through coordination by the Institute of Virology at the University of Saarland using the “XplOit” platform.

A basic version of the “Xploit” platform will be ready in autumn 2018 from the University Hospital and model developers. First prototype predictive models for stem cell transplantation medicine are expected in early 2019. XplOit is funded under the initiative i:DSem — Integrative Data Semantics in Systems Medicine funded by the Federal Ministry of Education and Research.

Duration: 01.03.2016 – 28.02.2021

Contact person(s):

Dr.-Ing. Gabriele Weiler / Dipl.-Inform. Stephan Kiefer
Projektkoordinatoren XplOit
Fraunhofer-Institut für Biomedizinische Technik IBMT
Joseph-von-Fraunhofer-Weg 1
66280 Sulzbach
Germany

Phone:+49 6894/980-156

E-Mail: gabriele.weiler@ibmt.fraunhofer.de / stephan.kiefer@ibmt.fraunhofer.de

Project partners:

Fraunhofer-Institut für Biomedizinische Technik IBMT, St. Ingbert (Koordinator)

Universität des Saarlandes

Max-Planck-Institut für Informatik, Saarbrücken

Universitätsklinikum Essen

Averbis GmbH, Freiburg