A novel way to manage and represent unstructured clinical data

on YouTube ↗37m

" summary

An automated pipeline extracts text from PDFs with Document AI, runs the Google Healthcare NLP API to pull entities, relationships, temporality and a per-entity confidence score, maps linked concepts from 17 vocabularies into a FHIR bundle (with Device/DeviceDefinition, Provenance and extensions for confidence and offsets), persists results to BigQuery and Firestore, and surfaces dashboards; handles paper-to-FHIR at scale (example dashboard finds three COPD phrase variants) and leaves low-confidence items for downstream review.

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" chapters (26)

1. 0:00Introduction and agenda
2. 0:23Overview of Google Healthcare NLP API
3. 1:03Unstructured data challenges in healthcare
4. 3:10Knowledge extraction from clinical text
5. 4:13Context assessment and certainty analysis
6. 5:17Standard vocabulary mapping with NLP
7. 6:00Live demo of NLP API in action
8. 8:05Mapping NLP output to FHIR resources
9. 10:57Using FHIR extensions for NLP insights
10. 13:04Complete FHIR bundle example
11. 14:53Building scalable data pipelines
12. 16:17Automating NLP with Apache Beam
13. 17:21Preprocessing PDFs with Document AI
14. 18:24End-to-end automation example
15. 21:17Storing NLP results in BigQuery and Firestore
16. 21:58Creating dashboards from NLP insights
17. 23:45Summary and ecosystem integration
18. 24:50Q&A: Market standards and vocabularies
19. 27:20Q&A: Provenance and NLP source indicators
20. 28:02Q&A: Context and patient narrative importance
21. 29:27Q&A: Common NLP use cases in healthcare
22. 31:19Q&A: Structured vs unstructured data integration
23. 33:04Q&A: NLP for non-clinical documents
24. 33:51Q&A: Using confidence scores in practice
25. 35:18Q&A: Language and vocabulary support
26. 36:44Q&A and closing remarks

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