{Reference Type}: Journal Article
{Title}: A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry.
{Author}: Macri CZ;Teoh SC;Bacchi S;Tan I;Casson R;Sun MT;Selva D;Chan W;
{Journal}: Graefes Arch Clin Exp Ophthalmol
{Volume}: 261
{Issue}: 11
{Year}: 2023 Nov 3
{Factor}: 3.535
{DOI}: 10.1007/s00417-023-06190-2
{Abstract}: <B>OBJECTIVE: </B>Advances in artificial intelligence (AI)-based named entity extraction (NER) have improved the ability to extract diagnostic entities from unstructured, narrative, free-text data in electronic health records. However, there is a lack of ready-to-use tools and workflows to encourage the use among clinicians who often lack experience and training in AI. We sought to demonstrate a case study for developing an automated registry of ophthalmic diseases accompanied by a ready-to-use low-code tool for clinicians.<BR><B>METHODS: </B>We extracted deidentified electronic clinical records from a single centre's adult outpatient ophthalmology clinic from November 2019 to May 2022. We used a low-code annotation software tool (Prodigy) to annotate diagnoses and train a bespoke spaCy NER model to extract diagnoses and create an ophthalmic disease registry.<BR><B>RESULTS: </B>A total of 123,194 diagnostic entities were extracted from 33,455 clinical records. After decapitalisation and removal of non-alphanumeric characters, there were 5070 distinct extracted diagnostic entities. The NER model achieved a precision of 0.8157, recall of 0.8099, and F score of 0.8128.<BR><B>CONCLUSIONS: </B>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.