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AI based Multi-lingual Text Mining for e-Health

Listing Description

Result description

We have developed Deep Learning methods for: Knowledge Graph Completion and Embeddings, Named Entity framework based on transfer learning (called T2NER), MedDistant19, MEDDISTANT19, a benchmark for broad-coverage biomedical relation extraction, a method for few-shot cross-lingual transfer for coarse-grained de-identification of code-mixed clinical texts.

Missions
Radical Innovation Breakthrough
Artificial Intelligence, Bioinformatics.

Addressing target audiences and expressing needs

Our needs are
  • We are sharing our knowledge
We specifically need / are looking for

We are looking for research institutes (academia and industry) who are interested in exploring and developing NLP and Machine Learning for digitalisation and machine understanding of clinical text in order to support future interaction and communication with and about information in the e-Health are.

Target audiences
  • Others/ No specific audience
  • Research and Technology Organisations

R&D, Technology and Innovation aspects

Current stage description

Available software package and data via github for LowerFER, T2NER, MedDistant19

Target Audiences: ‘Private Investors’ and or/ ‘Public or private funding

Replicability

Software is available as well as standard data test on which systems have been trained and tested.

Result submitted to Horizon Results Platform by DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH

Offer Details

Specifications and Deliverables:

Value Proposition:

We focus on cross-lingual and cross-domain applications in a low-resource setting.

Input Required from Customer:

Fields of Application:

Patent/Publication Number

Main Patent Claims:

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Adress:

TRIPPSTADTER STRASSE 122
Kaiserslautern
Rhineland-Palatinate
67663
Germany

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