Das Unternehmen entwickelt Entscheidungs-Unterstützungs Tools für Ärzte und medizinisches Personal.
Dafür mussten Inhalte von Medikamenten- Beipackzetteln in großer Menge verarbeitet werden und die Textinhalte semantisch analysiert werden.
Dazu ist eine Infrastruktur und Datenarchitektur zum Text Mining notwendig, die die Transformation von unterschiedlichen Textinhalten für die Verarbeitung von Machine Learning Algorithmen ermöglicht.
Lösung
Design der Architektur für die interne Natural Language Processing (NLP) und Text Mining Plattform
Text Processing Pipeline zur Transformation von unstrukturierten Textinhalten in Datenformate, die zur Verarbeitung durch Natural Language Processing (NLP) Algorithmen geeignet sind
Textbereinigung und Preprocessing (Tokenization, Vectorization, Stemming, Frequency Matrizen, Embeddings)
Ergebnisse
Aufbau Text Mining Architektur
Datenmodellierung für Machine Learning
Natural Language Processing Best Practices
Haben Sie Fragen?
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