Systemic lupus in the era of machine learning medicine

Figures

• 

  Figure 1. Categories of machine learning. Machine learning is a type of artificial intelligence. Within machine learning, there are three main categories: supervised, unsupervised and reinforcement learning. Deep learning is a subtype of machine learning that can involve supervising, unsupervised or reinforcement learning. Within each category, there are many different types of machine learning algorithms. Many factors can influence the choice of a specific algorithm. These include amount and type of data (eg, if images or videos are included in the data, a neural network will probably be preferred); how important interpretability is to your context (eg, decision trees or regression models are typically more interpretable, although this is an active area of research); and any computer memory or computational restriction. As no particular model consistently performs better than the others, it is typical to develop several models using multiple algorithms and then compare their performance using different metrics. CNN, convolutional neural network; CVD, cardiovascular disease; LASSO, least absolute shrinkage and selection operator; RNN, recurrent neural network.

• 

  Figure 2. Machine learning pipeline with consideration of the ethical, governance and regulation issues at every stage before clinical adoption of the model.

• 

  Figure 3. Number of SLE-related studies using machine learning methods. There has been an exponential growth of reports over the past 31 years based on PubMed database of publications when we searched ‘machine learning’ and ‘lupus’. The majority of reports were related to diagnosis (including neuropsychiatric and dermatological manifestations), followed by disease activity (including renal flares, extrarenal flares and treatment response), complications, pathogenesis, and mixed reports.