Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In this Quest you will use the Cloud Healthcare API to ingest and process data in the industry standard FHIR, HL7v2 and DICOM formats, train a TensorFlow model for prediction with FHIR data, and also gain practice with de-identification of datasets.
Overview
Syllabus
- Ingesting FHIR Data with the Healthcare API
- In this lab you will learn the basic functionalities of Cloud Healthcare API using the Fast Healthcare Interoperability (FHIR) data model.
- Machine Learning Predictions with FHIR and Healthcare API
- In this lab you will create a prediction pipeline for FHIR resources using Cloud Healthcare API and AI Platform.
- Ingesting HL7v2 Data with the Healthcare API
- In this lab you will discover and use the basic functionality of Cloud Healthcare API using HL7v2 messages.
- Ingesting DICOM Data with the Healthcare API
- In this lab you will learn the basic functionality of Cloud Healthcare API using Digital Imaging and Commmuniations in Medicine (DICOM) data model.
- De-identifying DICOM Data with the Healthcare API
- Use the de-identification functionality of Cloud Healthcare API with the Digital Imaging and Communications in Medicine (DICOM) data model.
- Streaming HL7 to FHIR Data with Healthcare API
- In this lab, you will explore some of the features of Cloud Healthcare API (HCAPI) to stream HL7v2 messages into HCAPI datastores and convert HL7v2 to FHIR and import the FHIR data into BigQuery for analytical use.
