Clinical free text to HPO codes

Rare Pub Date : 2023-01-01 DOI:10.1016/j.rare.2023.100007

Gabrielle Stinton , Jane A. Lieviant , Sylvia Kam , Jiin Ying Lim , Jasmine Chew-Yin Goh , Weng Khong Lim , Gareth Baynam , Tele Tan , Duc-Son Pham , Saumya Shekhar Jamuar

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Abstract

Leveraging Artificial Intelligence (AI) within the rare disease diagnostic odyssey can facilitate a decrease in diagnostic times and an increase in diagnostic rates. Among the steps involved in the odyssey, this project focused on utilizing AI to automate the standardized capturing of clinical free text into Human Phenotype Ontology (HPO) codes. This research project was conducted at both the KK Women’s and Children’s Hospital (KKH), Singapore and the Rare Care Centre at Perth Children’s Hospital, Western Australia (WA), via the Curtin New Colombo Plan (NCP) Scholarship. The outcome of the project saw the development of a Streamlit web application that utilized two (2) pre-trained AI models – PhenoTagger and PhenoBERT – with a human-in-the-loop design. A case study conducted with ten (10) de-identified clinical reports demonstrated a reduction in the HPO extraction task time from ten (10) to twenty (20) minutes per report to less than five (5) minutes.

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临床免费文本到HPO代码

在罕见病诊断过程中利用人工智能(AI)可以缩短诊断时间，提高诊断率。在奥德赛所涉及的步骤中，该项目侧重于利用人工智能自动将临床自由文本标准化捕获为人类表型本体(HPO)代码。该研究项目是通过科廷新科伦坡计划(NCP)奖学金在新加坡KK妇女儿童医院(KKH)和西澳大利亚州珀斯儿童医院(WA)的罕见护理中心进行的。该项目的成果是开发了一个Streamlit web应用程序，该应用程序使用了两个预训练的人工智能模型——PhenoTagger和PhenoBERT——并采用了人在循环的设计。一项由十(10)份去识别临床报告进行的案例研究表明，HPO提取任务时间从每份报告的十(10)到二十(20)分钟减少到不到五(5)分钟。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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