大数据时代的机器学习智能医疗保健系统:应用,诊断见解,挑战和伦理影响
Sita Rani1, Raman Kumar2,3, B S Panda4
1Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India.
Diagnostics (Basel, Switzerland)
|August 14, 2025
概括
智能医疗系统利用大数据和机器学习 (ML) 来实现个性化的患者护理和改进的诊断. 本综述综合了它们的融合,解决了挑战,并展示了用于增强临床决策的现实应用.
科学领域:
- * 卫生信息学和人工智能
- * 医疗保健中的数据科学
背景情况:
- * 医疗数据的快速增长需要先进的分析方法.
- * 患者对定制和有效的医疗保健服务的需求不断增加.
- * 以往专注于人工智能或大数据的审查的局限性.
研究的目的:
- * 综合大数据和机器学习 (ML) 在智能医疗保健中的融合.
- * 探索诊断,运营效率和个性化患者护理方面的应用.
- * 批判性地讨论智能医疗保健系统的伦理整合和挑战.
主要方法:
- *对现实世界的案例研究和跨领域的ML应用进行审查.
- *分析大数据分析和ML技术 (深度学习,NLP).
- *研究先进的方法,如联合学习 (FL) 和边缘计算,以及IoMT和可穿戴设备.
主要成果:
- * ML方法在疾病预测,医学成像和药物发现等领域增强了临床决策.
- * 大数据分析和机器学习提高了诊断准确度 (高达95%) 和运营效率.
- *可穿戴设备和IoMT支持实时,数据驱动的医疗保健服务.
结论:
- * 大数据和机器学习的整合为智能医疗提供了革命性的潜力.
- *解决数据异质性,隐私和计算复杂性等挑战至关重要.
- *未来的方向集中在可扩展,道德和可解释的AI驱动的医疗保健系统上.
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