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  • Review Article
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An introduction to machine learning and analysis of its use in rheumatic diseases

Nature Reviews Rheumatology volume 17, pages 710–730 (2021)Cite this article

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Abstract

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Key points

  • Appropriate application of machine learning (ML) algorithms and model construction, including that using data from patients with rheumatic autoimmune inflammatory diseases (RAIDs), involves preprocessing, feature selection, comparisons of multiple models to determine which is most appropriate for the data, and proper validation.

  • ML has been applied to various types of data from patients with RAIDs, including medical records and imaging data to classify patients, sequencing data to predict genetic risk loci, biometric data to identify disease activity, transcriptomic data to classify or cluster patient subtypes, and demographic, genetic and genomic data to predict treatment response.

  • Most published studies that describe the employment of ML in RAIDs, however, only serve as proof-of-principle studies as they lack adequate sample sizes or external test datasets; consequently, clinical translation of ML in rheumatology is in a nascent stage.

  • Current ML studies provide hypotheses that can be validated in large retrospective datasets or used to design prospective trials characterized by correct data collection and sample sizes that are suitable for the application of ML.

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Fig. 1: The output of a machine learning model is classification, regression or clustering.
Fig. 2: Machine learning model workflow.
Fig. 3: Guidelines for selecting the most appropriate machine learning algorithm.
Fig. 4: Receiver operating characteristic curves are used to assess binary classification performance.

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Acknowledgements

The authors thank P. Bachali, S. Shrotri, K. Bell, and J. Kain for helpful discussion about machine learning concepts. The authors thank Dr. C. Nantasenamat for allowing us to modify his figure about the workflow of ML. This work was supported by funding from the RILITE Foundation.

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  1. AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA

    Kathryn M. Kingsmore, Christopher E. Puglisi, Amrie C. Grammer & Peter E. Lipsky

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Contributions

K. M. K. and C. E. P. researched data for the article. K. M. K., C. E. P., A. C. G. and P. E. L. contributed substantially to discussion of the content. K. M. K., C. E. P. and P. E. L. wrote the article. K. M. K. C.E. P. and P. E. L. reviewed and/or edited the manuscript before submission.

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Correspondence to Kathryn M. Kingsmore.

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Competing interests

K.M.K. and C.E.P. were employed by AMPEL BioSolutions, LLC, during the preparation of this work. K.M.K. was additionally employed by the RILITE Research Institute during the preparation of this work. A.C.G. and P.E.L. are the founders of AMPEL BioSolutions, LLC. The authors declare that the content of this manuscript is not related to AMPEL BioSolutions, LLC’s commercial activities. AMPEL uses machine learning as one technique in our analyses pipelines, but does not have a proprietary interest in machine learning as a technology or commercial interest in a specific classifier, regressor or clustering approach. All of the material described in the manuscript is freely available in the public domain.

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Nature Reviews Rheumatology thanks M. Krusche and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Machine learning

(ML). A subset of artificial intelligence that utilizes software to predict outcomes and recognize relationships in data without explicit programmes for each step.

Algorithms

Mathematical or computational methods that can be applied to data to form a model.

Model

A framework built upon input data that can classify, regress or cluster.

Statistical modelling

A model that relies on explicitly programmed mathematical functions to explain relationships in data.

Classification

Prediction of a categorical outcome.

Regression

Prediction of a quantitative outcome.

Clustering

Grouping of data points with similar characteristics.

Labelled

Data for which the class or outcome value is known.

Class

A group with a label that is produced from classification.

Clusters

Groups without a label that are produced from clustering.

Supervised models

Models trained on labelled data that are used to predict classes or quantitative values.

Unsupervised models

Models trained on unlabelled data that are used to find associations and patterns that result in groups of similar samples.

Imputation

A method of replacing missing values with data points.

Data scaling

The processes of transforming data into a format that a computer algorithm can use, which can also involve normalization.

Feature selection

The process of selecting the best set of variables to be used as input for the model.

Natural language processing

(NLP). A data scaling process that is also a branch of ML, which allows computers to interpret human language.

Dimensionality reduction

The process of reducing the number of input variables (features).

Variance

Error as a result of the fluctuations in the observations, or how much the observations differ from the average value.

Biased

A biased model is one that fails to capture underlying patterns in data and thus there is a difference between the true values and the values predicted by the model.

Decision trees

Supervised method that asks a series of ‘yes or no’ questions with labelled data to classify or regress.

Clustering algorithms

Unsupervised methods that assign observations to subsets using mathematically calculated distances.

Neural networks

Supervised or unsupervised methods that build a series of networks to predict or classify. They are named because the structure of the model is aimed at mimicking the way in which a human brain operates.

Ensemble algorithms

Supervised methods that aggregate several predictors from multiple machine learning models (for example, random forest).

Bagging

Algorithm that generates training sets by sampling of the training data with replacement to generate individual models that are characteristic of the sample, which are then aggregated to build a final model.

Boosting

Algorithm that adds an additional simpler model to minimize the existing error during each iteration of a supervised model.

Bayesian algorithms

Supervised methods that solve classification problems by predicting the most probably hypothesis, given the input data (for example, naive Bayes).

Instance-based

Supervised methods that memorize instances seen in training to make predictions (for example, support vector machines and k-nearest neighbours).

Regression algorithms

Supervised methods that use linear or polynomial functions for or as a fundamental part of prediction (for example, linear regression and logistic regression).

Regularization algorithms

A type of supervised regression method that shrinks coefficient estimates to zero to avoid overfitting (for example, least absolute shrinkage and selection operator and ridge regression).

Hyperparameters

Variables that must be set prior to model construction by the user or by software default and can then be tuned during model construction to maximize accuracy.

Parameters

Variables that are ‘learned’ during model construction. Parameters differ between algorithms based on algorithm architecture.

Training dataset

The dataset used by supervised models to ‘learn’ to predict an outcome by viewing both the input and output variables in the data.

Validation dataset

A portion of the training dataset that is withdrawn to give an estimate of fit while tuning model parameters, or a separate dataset used to estimate model fit and tune parameters.

Holdout

The process of reserving some samples for training and some for validation from a single dataset.

k-fold cross-validation

An extension of model validation that partitions the data into complementary subsets when training, to perform parallel analyses on each subset.

Sensitivity

The proportion of the actual positives that are correctly identified. Also known as the true positive rate.

Specificity

The proportion of the actual negatives that are correctly identified. Also known as the true negative rate.

Receiver operating characteristic (ROC) curves

(ROC curve). A plot of the sensitivity against the 1 − specificity that is used to assess the performance of a binary classifier.

Area under the curve

(AUC). Generally refers to the area under the ROC curve, so it can also be referred to as the area under the ROC (AUROC).

Testing dataset

An independent dataset that is used to provide an unbiased evaluation of the final model fit.

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Kingsmore, K.M., Puglisi, C.E., Grammer, A.C. et al. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat Rev Rheumatol 17, 710–730 (2021). https://doi.org/10.1038/s41584-021-00708-w

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