预防性医疗保健中的机器学习应用:从多个角度对疾病共患病的预测分析进行系统文献综述
1School of Economics and Management, Southeast University, Nanjing 211189, China.
Artificial intelligence in medicine
|August 20, 2024
概括
人工智能 (AI) 和机器学习正在推动疾病并发症研究. 这些方法改善了诊断,并确定了风险因素,以更好地管理慢性疾病.
科学领域:
- 生物医学研究的研究.
- 医疗保健管理的管理
- 人工智能的人工智能是人工智能.
背景情况:
- 疾病并发症对患者的生活质量构成重大风险,特别是在中年和老年人群中.
- 多种慢性疾病使准确的诊断变得复杂,并使医疗保健资源受到压力.
- 大量的健康数据为机器学习应用提供了机会.
研究的目的:
- 审查当前关于应用人工智能和机器学习来了解疾病并发症的研究.
- 分析研究进展,并确定该领域的关键预测分析任务.
- 为未来的并发症研究,慢性疾病预防和诊断提供见解.
主要方法:
- 791个出版物的描述性元数据分析 (2012年1月 - 2023年6月).
- 对61篇以共发病为重点的科学论文进行系统评估.
- 确定四个预测分析任务:数据提取,聚类,网络分析和风险预测.
主要成果:
- 机器学习应用程序解决医疗保健数据缺陷.
- 模型解释识别了伴随性疾病发展的重大风险因素.
- 研究表明,在应用人工智能来理解复杂疾病模式方面,有进展.
结论:
- 人工智能和机器学习为分析疾病并发症提供了强大的工具.
- 这项研究指导了未来预防和诊断慢性疾病的努力.
- 对医疗保健系统的管理影响来自于人工智能驱动的洞察力.
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