A case study in applying artificial intelligence-based named entity recognition to develop an automated ophthalmic disease registry

Abstract

Purpose 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. Methods 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. Results 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. Conclusion 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.

The Smart Document Processing with Artificial Intelligence

“…RPA, also known as digital employee, has become a powerful tool to facilitate the automatic processing of business documents. RPA shows its effectiveness by enabling simple configuration of robots to handle high-volume and repetitive tasks automatically based on predefined rules which not only limits the human errors, but also improves the efficiency and cost reduction [ According to [12], significant improvements have been made in extracting information from free text using NLP techniques based on AI. NLP uses syntactic rules to automatically scan and extract named entities like names, locations, organizations, dates, and invoice numbers from unstructured text using NER.…”