Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of machine learning (ML), these factors are known as domains and the distribution differences they cause in the data are known as domain shifts. ML models trained using data from one domain often perform poorly when applied to data from another domain, potentially leading to wrong predictions. As such, developing machine learning models that can generalise well across multiple domains is a challenging yet essential task in the successful application of ML in clinical practice. In this paper, we propose a novel disentangled autoencoder (Dis-AE) neural network architecture that can learn domain-invariant data representations for multi-label classification of medical measurements even when the data is influenced by multiple interacting domain shifts at once. The model utilises adversarial training to produce data representations from which the domain can no longer be determined. We evaluate the model's domain generalisation capabilities on synthetic datasets and full blood count (FBC) data from blood donors as well as primary and secondary care patients, showing that Dis-AE improves model generalisation on multiple domains simultaneously while preserving clinically relevant information.

翻译：临床数据常受测量设备差异或不同站点处理方法不同等临床无关因素影响。在机器学习领域，这些因素被称为域，其导致的数据分布差异被称为域偏移。使用某一域数据训练的机器学习模型应用于另一域数据时往往表现不佳，可能导致错误预测。因此，开发能够在多个域间良好泛化的机器学习模型，是临床应用机器学习中一项具有挑战性但至关重要的任务。本文提出一种新颖的解耦自编码器（Dis-AE）神经网络架构，该架构能学习域不变的数据表示，用于医疗测量的多标签分类，即使数据同时受多种交互域偏移影响。该模型采用对抗训练生成无法再确定域归属的数据表示。我们在合成数据集以及来自献血者、初级和二级护理患者的全血细胞计数（FBC）数据上评估了模型的域泛化能力，结果表明Dis-AE能在保留临床相关信息的同时，同时提升模型在多个域上的泛化性能。