We help Life Sciences companies using full potential of their distributed and unstructured data.
What happens in a specific region or patent area? Which biomarkers can provide information for prognosis and individual therapy? How can we monitor drug safety more efficient? How can we speed up drug development?
Working with pharma, biotech and medtech companies over the last 13 years Averbis provides a preconfigured and easy to enhance text mining and machine learning platform that serves the complete value chain from research, regulatory affair to marketing.
We help our customers to integrate AI solutions quickly into their own processes and stay flexible.
Companies are faced with an overwhelming amount of electronic publications to be collected, archived and indexed in library collections and made available to the public.
This increasing volume in documents cannot be managed any more in forms of intellectual processing.
Averbis supports companies with automated keyword indexing and document categorization of the repositories, to
- significantly reduce the time, effort and costs of indexing
- overcome previous existing indexing gaps in high quality
- better support information retrieval
With the use of freely selectable terminologies and ontologies, keywords and descriptors are automatically extracted from text. Averbis provides a wealth of publicly available terminologies for this task. Text mining technologies identify main headings in documents for content-related structuring and for improved searchability.
Articles and text documents are automatically classified for categorization using freely-definable category systems. Documents and collections may automatically be assigned to the resorts they belong to (e.g. ‘economy’, ‘politics’, ‘medicine’).
Monitoring of competitors, contractors, or certain products is a high demand in various industries. Analyzing patent portfolios is one strategy to complement this complex puzzle. Such an analysis comprises the detailed assessment of large patent corpora to specific technology fields. Assessments such as technology fields, however, go often beyond the typical patent meta-data such as International Patent Classifications (IPCs) or other classification schemes.
Furthermore, the technology fields to be analyzed may be taken from a company internal thesaurus with possibly large number of concepts as well as hierarchical relations between them. Still, the classification of a considerable fraction of patent portfolios according to such application-specific thesauri is a non-trivial task that requires a significant amount of human expert man power.
Therefore, there is a pressing need for decision support systems that assist human experts in this complex annotation task and at the same time learn from previous expert annotations to speed up the landscaping process.
- Save time and cost through automatic extraction of the most important elements for IDMP with >90% correctness
- Increased data quality through automatic, consistent assignment and coding of controlled vocabulary
- Handle change requests easily by automatic re-extraction
- Easy to integrate into your existing IDMP system/workflow
Information Discovery contains a multilingual terminology management system to support IDMP controlled vocabularies populated and maintained by the European Medical Agency.
If you want to learn more about text mining for regulatory intelligence take a closer look at “Text mining for regulatory intelligence: taking an automated approach” our esteemed colleagues Harsha Gurulingappa, Text Analytics Product Owner, IT Advanced Analytics, Dominik Schneider, Senior Architect, IT Advanced Analytics, Moritz Kloft, Senior IT Project Manager, IT Healthcare, all at Merck KGaA, Darmstadt, Germany; and Janaki Suriyanarayanan, Senior Manager – Regulatory Information Management, J Joerg Werner, Associate Director – Regulatory Data Governance, both at Global Regulatory Affairs, Merck Healthcare KGaA wrote for Regulatory Rapporteur November 2020.
PROVISION OF STANDARD AND CUSTOM TERMINOLOGIES
Terminologies play a crucial role whenever specialized knowledge is created, communicated, maintained and accessed. Terminology management has become an integral part of business processes aiming at increasing productivity, quality of products and services and user satisfaction.
Averbis provides world class terminology work for pharmaceutical companies. We provide all relevant standard terminologies for Life Sciences. We also create company tailored und use case specific terminologies for maximum terminology experience.
MAPPINGS BETWEEN TERMINOLOGIES
Terminologies and ontologies declare sets of concepts and relations to encode meaning. They establish a more effective means of capturing and using structured and computable data to improve management of outcomes and reduce costs. Mappings between terminologies serve as semantic glue between applications and help breaking down data silos in companies. They are essential for an effective company-wide information infrastructure. Averbis provides high quality terminology mappings for your most relevant terminologies.
CREATION & ENRICHMENT OF TERMINOLOGIES
All terminologie are not complete and subject to constant change and enlargement. Incomplete terminologies cause negative user experience in terminology-based applications. We enrich terminologies, both automatically and manually. This leads to better data quality, more hits of your searches and more successful applications:
Terminology 100 % Hits Enriched 150 % Hits
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 laboratory values in lab records, medications and diagnoses for health care scenarios) are available as add-ons.
Scientists can easily extend Information Discovery to tailor the text analysis functionality to their specific needs. It combines state of the art machine learning, terminologies and a powerful rule engine to uncover facts and relations in unstructured text data.
We have chosen Averbis as our text mining platform because the solution is highly flexible with an open framework, is a strategic match to our architecture, installed on premise, and proved to be ahead of the competition in our text mining challenge.
Now we are glad to have Averbis as a partner for our pharmaceutical use cases. Averbis showed an agile style of working and a high dedication to quality which brought our project to a fast success.
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