  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.
