While machine learning algorithms hold promise for personalised medicine,
their clinical adoption remains limited. One critical factor contributing to
this restraint is sample selection bias (SSB) which refers to the study
population being less representative of the target population, leading to
biased and potentially harmful decisions. Despite being well-known in the
literature, SSB remains scarcely studied in machine learning for healthcare.
Moreover, the existing techniques try to correct the bias by balancing
distributions between the study and the target populations, which may result in
a loss of predictive performance. To address these problems, our study
illustrates the potential risks associated with SSB by examining SSB's impact
on the performance of machine learning algorithms. Most importantly, we propose
a new research direction for addressing SSB, based on the target population
identification rather than the bias correction. Specifically, we propose two
independent networks (T-Net) and a multitasking network (MT-Net) for addressing
SSB, where one network/task identifies the target subpopulation which is
representative of the study population and the second makes predictions for the
identified subpopulation. Our empirical results with synthetic and
semi-synthetic datasets highlight that SSB can lead to a large drop in the
performance of an algorithm for the target population as compared with the
study population, as well as a substantial difference in the performance for
the target subpopulations that are representative of the selected and the
non-selected patients from the study population. Furthermore, our proposed
techniques demonstrate robustness across various settings, including different
dataset sizes, event rates, and selection rates, outperforming the existing
bias correction techniques.

机器学习算法在个性化医学中具有潜力，但其临床应用仍受到限制。研究表明，样本选择偏差（SSB）是限制其采用的一个关键因素。本研究通过检验 SSB 对机器学习算法性能的影响，指出了潜在的风险，并提出了一种新的研究方向，该方向基于目标人群的识别而非偏差校正来解决 SSB 问题。实验结果表明，SSB 可能导致算法在目标人群上的性能大幅下降，并且对于从研究人群中选取的代表性和非选取患者的目标子人群的性能存在显著差异。此外，我们提出的技术在不同数据集大小、事件率和选择率的各种场景下展现出稳健性，优于现有的偏差校正技术。

机器学习在医疗领域的样本选择偏差

Sample Selection Bias in Machine Learning for Healthcare

Obtaining reliable precipitation estimation with high resolutions in time and
space is of great importance to hydrological studies. However, accurately
estimating precipitation is a challenging task over high mountainous complex
terrain. The three widely used precipitation measurement approaches, namely
rainfall gauge, precipitation radars, and satellite-based precipitation
sensors, have their own pros and cons in producing reliable precipitation
products over complex areas. One way to decrease the detection error
probability and improve data reliability is precipitation data merging. With
the rapid advancements in computational capabilities and the escalating volume
and diversity of earth observational data, Deep Learning (DL) models have
gained considerable attention in geoscience. In this study, a deep learning
technique, namely Long Short-term Memory (LSTM), was employed to merge a
radar-based and a satellite-based Global Precipitation Measurement (GPM)
precipitation product Integrated Multi-Satellite Retrievals for GPM (IMERG)
precipitation product at hourly scale. The merged results are compared with the
widely used reanalysis precipitation product, Multi-Radar Multi-Sensor (MRMS),
and assessed against gauge observational data from the California Data Exchange
Center (CDEC). The findings indicated that the LSTM-based merged precipitation
notably underestimated gauge observations and, at times, failed to provide
meaningful estimates, showing predominantly near-zero values. Relying solely on
individual Quantitative Precipitation Estimates (QPEs) without additional
meteorological input proved insufficient for generating reliable merged QPE.
However, the merged results effectively captured the temporal trends of the
observations, outperforming MRMS in this aspect. This suggested that
incorporating bias correction techniques could potentially enhance the accuracy
of the merged product.

利用长短时记忆（LSTM）深度学习技术，在较高分辨率的时间和空间尺度上将基于雷达和卫星的全球降水测量产品合并，以有效捕捉观测的时间趋势并提高准确性，其中包括采用偏差修正技术以增强合并降水产品的准确性。