医疗保健应用机器学习模型的进步:精确且以患者为中心的方法
Bhumika Parashar1, Sathvik Belagodu Sridhar2, Kalpana3
1Department of Pharmacy, School of Medical and Allied Sciences, Galgotias University, Greater Noida, U.P., India.
Current pharmaceutical design
|February 14, 2025
概括
机器学习 (ML) 通过改进诊断和疫情预测来提高医疗保健. 解决诸如偏见和数据质量等挑战对于ML来说至关重要,它可以彻底改变患者护理和全球健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 医疗保健越来越多地利用机器学习 (ML) 进行患者护理,运营效率和复杂的医疗问题解决.
- 尽管存在道德和偏见问题,COVID-19大流行强调了ML在预测疾病爆发和优化治疗方面的价值,尽管存在道德和偏见问题.
研究的目的:
- 讨论机器学习在医疗保健领域的各种应用.
- 概述机器学习技术在医疗环境中所带来的重大好处.
- 检查阻碍在医疗保健中广泛采用ML的伦理和实际挑战.
主要方法:
- 本文提供了对医疗保健当前机器学习应用的全面审查.
- 它综合了有关这些技术优势和局限性的信息.
- 讨论包括对道德考虑和实际实施障碍的分析.
主要成果:
- 机器学习在临床诊断,持续的患者监测和流行病学预测方面具有实用性.
- 发现的关键挑战包括算法偏见,数据完整性问题以及对强有力的模型验证的需求.
- 有效的缓解策略包括确保高质量的数据集,开发公正的算法和持续的绩效监测.
结论:
- 机器学习为医疗保健提供了变革的潜力,有望提高效率和改善患者的治疗结果.
- 充分的整合和进步需要解决道德困境,实际障碍和技术限制.
- 解决这些多方面的问题对于实现ML的全球健康益处至关重要.
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