临床决策中的人工智能:对基于规则的系统及其在医学中的应用进行全面审查
Ashraf Alnattah1,2, Mahdie Jajroudi1,2, Seyyed Ali N Fadafen1
1Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, IRN.
临床决策支持系统 (CDSS) 中的规则引擎提高了患者的护理和效率. 尽管在采用和互操作性方面存在挑战,但混合人工智能方法对未来的医疗整合有希望.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 医疗保健服务研究 医疗服务研究
背景情况:
- 基于规则的临床决策支持系统 (CDSS) 是医疗保健中成熟的AI应用.
- 这些系统使用规则引擎与预定义的逻辑 (如果/然后规则) 报警和建议.
- 需要对它们的应用,有效性和演变进行全面的综合.
研究的目的:
- 检查医学中规则引擎实现的景观.
- 专注于临床功能,评估结果,技术特征和地理采用.
- 分析规则引擎使用的时间趋势和地理分布.
主要方法:
- 覆盖范围审查方法.
- 在PubMed和Scopus数据库 (2007-2023) 中进行系统搜索.
- 在选了437个记录后,包括28项研究;提取研究特征,应用,技术和结果的数据.
主要成果:
- 美国主导研究 (46.42%);实现从SQL演变为ML/NLP集成.
- 在慢性疾病管理中有效 (30%的糖尿病治疗),减少30%的不良药物事件.
- 在系统互操作性和临床医生采用方面发现的挑战.
结论:
- 规则引擎显示出临床决策和医疗保健效率的巨大潜力.
- 收养在地理上不均,在技术上受到限制.
- 建议包括标准化框架,在代表性不足的地区扩大研究,以及混合人工智能系统.
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