For healthcare datasets, it is often not possible to combine data samples from multiple sites due to ethical, privacy or logistical concerns. Federated learning allows for the utilisation of powerful machine learning algorithms without requiring the pooling of data. Healthcare data has many simultaneous challenges which require new methodologies to address, such as highly-siloed data, class imbalance, missing data, distribution shifts and non-standardised variables. Federated learning adds significant methodological complexity to conventional centralised machine learning, requiring distributed optimisation, communication between nodes, aggregation of models and redistribution of models. In this systematic review, we consider all papers on Scopus that were published between January 2015 and February 2023 and which describe new federated learning methodologies for addressing challenges with healthcare data. We performed a detailed review of the 89 papers which fulfilled these criteria. Significant systemic issues were identified throughout the literature which compromise the methodologies in many of the papers reviewed. We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.

Survival analysis is an integral part of the statistical toolbox. However, while most domains of classical statistics have embraced deep learning, survival analysis only recently gained some minor attention from the deep learning community. This recent development is likely in part motivated by the COVID-19 pandemic. We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning. On the one hand, we discuss how survival analysis connects to classification and regression. On the other hand, we provide technical tools. We provide a new loss function, evaluation metrics, and the first universal approximating network that provably produces survival curves without numeric integration. We show that the loss function and model outperform other approaches using a large numerical study.

Machine learning has promised to revolutionise healthcare for several years [1, 2]. Moreover, while there is an extensive literature describing high-performing machine learning models trained on immaculate benchmark datasets [3-5], such promising approaches rarely make it into clinical practice [6]. Often, this is because of an unexpected drop in performance when deploying the model on unseen test data due to domain shift [7, 8], i.e. there is a change in the data distribution between the dataset a model is trained on (source data) and that which it is deployed against (target data). Most common machine learning algorithms rely on an assumption that the source and target data are independent and identically distributed (i.i.d.) [9]. However, with domain shift, this assumption no longer holds, and model performance can be significantly affected. For medical datasets, domain shift is widespread, resulting from differences in equipment and clinical practice between sites [10-13], and models are vulnerable to associating clinically irrelevant features specific to the domain with their predictions, known as shortcut learning [14], which may lead to poor performance on target data. For most medical applications, target data is rarely available prior to real-time deployment; thus, a domain adaptation approach, where pre-trained models are fine-tuned on data from the target distribution is not feasible.

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow - not biphasically plan and build - DSSs, and one should always employ two types of feedback loops during development: one which tests the code's correctness and another that evaluates the code's efficacy. Machine learning is in a reproducibility crisis [Hai+20; the code produces identical results - for replicability and Pin+21; Bak16]. We argue that a primary driver is poor code general reproducibility using independent implementations, quality, having two root causes: poor incentives to produce correctness is crucial.