摘要翻译：
在这篇论文中，我们应用计算机学习方法来诊断卵巢癌，使用标准生物标志物CA125的水平结合质谱提供的信息。我们正在使用一个新的数据集收集了7年的时间。利用CA125水平和质谱峰，我们的算法给出了疾病的概率预测。为了估计分类精度，我们将概率预测转换为严格预测。我们的算法比CA125电平和一个峰值强度的几乎任何线性组合（在对数尺度上）产生的误差都少。为了检验我们的算法的能力，我们用它来检验假设，即CA125和峰值不包含对诊断前特定时间的疾病预测有用的信息。我们的算法产生$P$-比以前应用于该数据集的算法产生的值更好。我们的结论是，所提出的算法对新数据的预测更可靠。
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英文标题：
《Online prediction of ovarian cancer》
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作者：
Fedor Zhdanov, Vladimir Vovk, Brian Burford, Dmitry Devetyarov, Ilia
  Nouretdinov and Alex Gammerman
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  In this paper we apply computer learning methods to diagnosing ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass-spectrometry. We are working with a new data set collected over a period of 7 years. Using the level of CA125 and mass-spectrometry peaks, our algorithm gives probability predictions for the disease. To estimate classification accuracy we convert probability predictions into strict predictions. Our algorithm makes fewer errors than almost any linear combination of the CA125 level and one peak's intensity (taken on the log scale). To check the power of our algorithm we use it to test the hypothesis that CA125 and the peaks do not contain useful information for the prediction of the disease at a particular time before the diagnosis. Our algorithm produces $p$-values that are better than those produced by the algorithm that has been previously applied to this data set. Our conclusion is that the proposed algorithm is more reliable for prediction on new data.
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PDF链接：
https://arxiv.org/pdf/0904.1579