利用生成性人工智能加速对罕见疾病的医疗行动的生物治疗
Enock Niyonkuru1,2, J Harry Caufield3, Leigh C Carmody2
1Trinity College, Hartford, CT, USA.
medRxiv : the preprint server for health sciences
|September 4, 2024
概括
自动MAxO使用大型语言模型 (LLM) 来自动化治疗罕见疾病的医疗行动,加速精准医学研究. 这种人工智能驱动的方法增强了医学行动本体学 (MAxO) 数据集,以提供更好的临床决策支持.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 罕见疾病研究研究.
背景情况:
- 结构化的临床数据和本体学对于翻译研究和计算分析至关重要.
- 医学行动本体学 (MAxO) 代表了患者的治疗和临床管理行动.
- 对MAxO的手动生物治疗是耗时的,并且很难在超过10,000种罕见疾病中进行扩展.
研究的目的:
- 开发和评估使用大型语言模型 (LLM) 的半自动化工作流程AutoMAxO,以简化罕见疾病的MAxO生物治疗.
- 提高为罕见疾病临床管理创建医疗行动结构化表示的效率和可扩展性.
主要方法:
- 自动MAxO使用LLMs从出版物摘要中提取候选医疗行动策划.
- 候选策划与本体学术语 (MAxO,HPO,MONDO) 相匹配,使用LLM和后处理.
- 匹配的术语由人类策展人审查和批准.
主要成果:
- 处理了4,918个独特的医学摘要,确定了21种罕见遗传疾病的注释.
- 提取了18631种候选疾病治疗疗法,其中538种已确认并添加到MAxO数据集中.
- 演示了生成人工智能的潜力,以加速生物医学文献的策划.
结论:
- 自动MAxO显著提高了MAxO生物治疗罕见疾病的效率.
- 生成型人工智能提供了一种强大的方法,通过结构化数据表示来加速精准医学.
- 自动MAxO方法可以适应其他生物医学治疗任务.
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