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