ai-toolkit-iot-edge/Biomedical entity recognition

IoT Scenario - Biomedical Entity Extraction

Biomedical named entity recognition is a critical step for complex biomedical NLP tasks such as: Extraction of diseases, symptoms from electronic medical or health records, drug discovery, understanding the interactions between different entity types such as drug-drug interaction, drug-disease relationship and gene-protein relationship.

Our scenario focuses on how a large amount of unstructured and unlabeled data such as PubMed article abstracts can be analyzed to train a domain-specific word embedding model. Then the output embeddings are used as automatically generated features to train a neural entity extraction model. The sample uses Keras with TensorFlow deep learning framework as a backend.

We have also included a Docker container with the final model. This container can be deployed to an IoT device via Azure IoT Hub.

Link to the Microsoft DOCS site

The detailed documentation for this real world scenario includes the step-by-step walkthrough:

https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-tdsp-biomedical-recognition

Link to the Azure Machine Learning Gallery GitHub repository

The public GitHub repository for this real world scenario contains all the code samples:

https://github.com/Azure/MachineLearningSamples-BiomedicalEntityExtraction