Breast cancer is a common and deadly disease, but it is often curable when
diagnosed early. While most countries have large-scale screening programs,
there is no consensus on a single globally accepted guideline for breast cancer
screening. The complex nature of the disease; the limited availability of
screening methods such as mammography, magnetic resonance imaging (MRI), and
ultrasound; and public health policies all factor into the development of
screening policies. Resource availability concerns necessitate the design of
policies which conform to a budget, a problem which can be modelled as a
constrained partially observable Markov decision process (CPOMDP). In this
study, we propose a multi-objective CPOMDP model for breast cancer screening
which allows for supplemental screening methods to accompany mammography. The
model has two objectives: maximize the quality-adjusted life years (QALYs) and
minimize lifetime breast cancer mortality risk (LBCMR). We identify the Pareto
frontier of optimal solutions for average and high-risk patients at different
budget levels, which can be used by decision-makers to set policies in
practice. We find that the policies obtained by using a weighted objective are
able to generate well-balanced QALYs and LBCMR values. In contrast, the
single-objective models generally sacrifice a substantial amount in terms of
QALYs/LBCMR for a minimal gain in LBCMR/QALYs. Additionally, our results show
that, with the baseline cost values for supplemental screenings as well as the
additional disutility that they incur, they are rarely recommended in CPOMDP
policies, especially in a budget-constrained setting. A sensitivity analysis
reveals the thresholds on cost and disutility values at which supplemental
screenings become advantageous to prescribe.

本研究提出了一种基于 CPOMDP 的多目标模型，用于乳腺癌筛查，该模型允许在乳腺 X 线摄影检查（mammography）的基础上使用辅助筛查方法。实验结果表明，使用加权目标的策略可以生成平衡的 QALY 和 LBCMR 值，并发现在预算有限的情况下，附加筛查的成本和不适感对于改善策略性能的影响较小。

一种多目标约束 POMDP 模型用于乳腺癌筛查

A multi-objective constrained POMDP model for breast cancer screening

We address the problem of extractive question answering using document-level
distant super-vision, pairing questions and relevant documents with answer
strings. We compare previously used probability space and distant super-vision
assumptions (assumptions on the correspondence between the weak answer string
labels and possible answer mention spans). We show that these assumptions
interact, and that different configurations provide complementary benefits. We
demonstrate that a multi-objective model can efficiently combine the advantages
of multiple assumptions and out-perform the best individual formulation. Our
approach outperforms previous state-of-the-art models by 4.3 points in F1 on
TriviaQA-Wiki and 1.7 points in Rouge-L on NarrativeQA summaries.

本文介绍了一种使用 document-level distant super-vision 解决抽取式问答问题的方法，该方法通过将问题及相关文档与答案字符串配对来实现。作者比较了概率空间和远程 supervisions assumptions 的区别，并证明了不同配置提供互补的益处。他们展示了一种多目标模型，可以高效地结合多个假设的优点并表现出更好的性能，超过了此前在 TriviaQA-Wiki 中各方面表现最好的模型 4.3 个 F1 点和 NarrativeQA 摘要中的 1.7 个 Rouge-L 点。