A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.
Carmelo Zak MacriSheng Chieh TeohStephen BacchiIan TanRobert CassonMichelle T SunDinesh SelvaWengOnn ChanPublished in: Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie (2023)
We presented a case study using low-code artificial intelligence-based NLP tools to produce an automated ophthalmic disease registry. The workflow created a NER model with a moderate overall ability to extract diagnoses from free-text electronic clinical records. We have produced a ready-to-use tool for clinicians to implement this low-code workflow in their institutions and encourage the uptake of artificial intelligence methods for case finding in electronic health records.