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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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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.

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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.

关键词:
人工智能在瘤学中的应用早期癌症检测 早期癌症检测妇科癌症 妇科癌症机器学习是机器学习.多主题整合多主题整合.个性化医疗是个性化的医疗.

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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.