Providing better medical decisions faster with natural language processing
Automatic interpretation of natural-language texts
Natural language processing also enables the analysis of unstructured data, e.g., radiological results, laboratory reports, scientific publications.
Automatic knowledge generation
Semi-supervised machine learning supports decision-making in close to real time and can be applied to large quantities of heterogeneous information of variable quality.
Rapidly improved business processes
Scalable cloud infrastructures, web APIs and stock interfaces allow easy integration into your system environment.
The industry-proven “Empolis Box” enables pseudonymization of data locally at your own workplace.
Initial results can be achieved quickly through an innovative combination of data-based methods of artificial intelligence and expert-approved structured templates from our partner Smart Reporting GmbH.
The Healthcare Analytics Services components
Healthcare Box – unstructured data stored in the cloud in compliance with data privacy laws
Healthcare Portal – natural language processing for efficient learning from historical cases
Healthcare Check – machine learning for assisted handling of new cases
The basis of intelligent healthcare applications
Flexible integration into system environments with cost transparency through scalable cloud infrastructures, web APIs and stock interfaces.
Central access to all relevant information through an innovative combination of artificial intelligence and expert-approved structured templates from our partner Smart Reporting GmbH.
Five reasons to use Healthcare Analytics Services
- Assisted diagnostics and treatment planning: Users are given recommendations, and they can reconstruct them on their own based on decision trees.
- Cost-efficient medical documentation: Documentation and invoices are generated automatically during a treatment process.
- Big Data Enabler: Medical texts are anonymized and analyzed as the basis for generating knowledge (e.g., deep learning via images) while complying with data privacy laws
- Connection of medical knowledge sources: Catalogues (BioPortal, Bio2RDF, etc.), terminology (WordNet, GermaNet, etc.), thesauri (MeSH, Wikipedia, etc.), ontologies (RadLex, FMA, GO, etc.).
- Knowledge modeling: Manually by medical experts (e.g., rules, decision trees) / automatically based on data (e.g., deep learning, ontology population)