预测妇科癌症的机器学习模型:进步,挑战和未来方向
Pankaj Garg1, Madhu Krishna2, Prakash Kulkarni2
1Department of Chemistry, GLA University, NH-19, Mathura-Delhi Road, Mathura 281406, Uttar Pradesh, India.
Cancers
|September 13, 2025
概括
机器学习 (ML) 提高了妇科癌症的早期检测和预测,改善了患者的治疗结果. 先进的AI模型分析复杂的数据,以进行个性化癌症护理和生存预测.
科学领域:
- 在瘤学瘤学.
- 生物医学数据科学 生物医学数据科学
- 机器学习 机器学习
背景情况:
- 妇科癌症 (乳腺癌,宫癌,卵巢癌) 由于非特异性症状和缺乏可靠的查,因此被发现较晚.
- 早期预测方法对于提高生存率,指导个性化治疗和减少医疗负担至关重要.
研究的目的:
- 审查最近机器学习 (ML) 模型在妇科瘤学中的瘤预测方面的进展.
- 突出AI驱动的ML在改善癌症查,风险分类和生存建模方面的潜力.
主要方法:
- 关于在妇科瘤学中ML应用的当前文献的综述.
- 讨论标准的ML算法 (SVM,随机森林) 和深度学习 (DL) 模型 (CNN).
- 探索新兴技术,如可解释的人工智能,联合学习 (FL) 和多omics融合.
主要成果:
- ML模型在癌症类型识别,进展监测和治疗设计方面具有很高的潜力.
- 人工智能模型可以整合各种数据集 (临床,基因组,成像) 来识别微妙的模式,以准确预测风险.
- 挑战包括数据不一致,模型可解释性和临床整合.
结论:
- ML正在为妇科癌症彻底改变精确瘤学,从而实现更好的以患者为中心的结果.
- 可解释的AI,FL和多omics融合是开发可靠和临床适用的ML模型的关键.
- ML的变革性作用有望改善对患有妇科癌症的妇女的护理.
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