PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：基于人工智能（AI）的命名实体提取（NER）的进展提高了从非结构化，叙事,电子健康记录中的自由文本数据。然而,缺乏现成的工具和工作流程来鼓励经常缺乏AI经验和培训的临床医生使用。我们试图证明一个案例研究，用于开发眼科疾病的自动化注册表，并为临床医生提供现成的低代码工具。
方法：我们从2019年11月至2022年5月的单中心成人门诊眼科诊所提取了去识别的电子临床记录。我们使用低代码注释软件工具（Prodigy）来注释诊断并训练定制的spaCyNER模型以提取诊断并创建眼科疾病注册表。
结果：从33,455份临床记录中提取了123,194份诊断实体。删除非字母数字字符后，共提取了5070个不同的诊断实体.NER模型的精度为0.8157，召回率为0.8099，F评分为0.8128。
结论：我们提出了一个案例研究，使用基于低代码人工智能的NLP工具来创建自动化的眼科疾病注册表。该工作流程创建了一个NER模型，具有从自由文本电子临床记录中提取诊断的中等总体能力。我们已经为临床医生制作了一个现成的工具，可以在他们的机构中实施这种低代码的工作流程，并鼓励采用人工智能方法在电子健康记录中进行病例查找。
方法：我们从2019年11月至2022年5月的单中心成人门诊眼科诊所提取了去识别的电子临床记录。我们使用低代码注释软件工具（Prodigy）来注释诊断并训练定制的spaCyNER模型以提取诊断并创建眼科疾病注册表。
结果：从33,455份临床记录中提取了123,194份诊断实体。删除非字母数字字符后，共提取了5070个不同的诊断实体.NER模型的精度为0.8157，召回率为0.8099，F评分为0.8128。
结论：我们提出了一个案例研究，使用基于低代码人工智能的NLP工具来创建自动化的眼科疾病注册表。该工作流程创建了一个NER模型，具有从自由文本电子临床记录中提取诊断的中等总体能力。我们已经为临床医生制作了一个现成的工具，可以在他们的机构中实施这种低代码的工作流程，并鼓励采用人工智能方法在电子健康记录中进行病例查找。
