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Machine-learning-based patient-specific prediction models for knee osteoarthritis

Nature Reviews Rheumatologyvolume 15pages4960 (2019) | Download Citation

Abstract

Osteoarthritis (OA) is an extremely common musculoskeletal disease. However, current guidelines are not well suited for diagnosing patients in the early stages of disease and do not discriminate patients for whom the disease might progress rapidly. The most important hurdle in OA management is identifying and classifying patients who will benefit most from treatment. Further efforts are needed in patient subgrouping and developing prediction models. Conventional statistical modelling approaches exist; however, these models are limited in the amount of information they can adequately process. Comprehensive patient-specific prediction models need to be developed. Approaches such as data mining and machine learning should aid in the development of such models. Although a challenging task, technology is now available that should enable subgrouping of patients with OA and lead to improved clinical decision-making and precision medicine.

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APPROACH: https://approachproject.eu

DataSHIELD: http://www.datashield.ac.uk/

Automatic Image Registration: http://air.bmap.ucla.edu/AIR5

tsfresh: http://tsfresh.readthedocs.io/en/latest

hctsa: https://github.com/benfulcher/hctsa

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Acknowledgements

The authors thank V. Wallis for her assistance with manuscript preparation.

Author information

Affiliations

  1. Osteoarthritis Research Unit, University of Montreal Hospital Research Centre (CRCHUM), Montreal, Quebec, Canada

    • Afshin Jamshidi
    • , Jean-Pierre Pelletier
    •  & Johanne Martel-Pelletier

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Contributions

J.M.-P. and A.J. researched data for the article. All authors wrote the article, made substantial contribution to discussions of the content and reviewed and/or edited the manuscript before submission.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Johanne Martel-Pelletier.

Supplementary Information

GlossaryGlossary terms

Artificial intelligence

The process of creating systems that can learn from experience and adjust to new inputs in order to perform human-like tasks. Machine-learning is a fundamental concept of artificial intelligence.

Calibration

Calibration measurements represent the level of accuracy of a model in estimating the absolute risk (that is, the agreement between the observed and predicted risk). Poorly calibrated models will underestimate or overestimate the outcome of interest.

Classification models

In statistics and machine-learning, classification is the process of identifying the category of a new observation on the basis of a training set of data containing observations for which the category (outcome value) is known. In the field of osteoarthritis, an example could be classification of patients into slow progressors and fast progressors on the basis of several input variables.

Deep-learning

A subfield of machine-learning that is based on advanced artificial neural networks; this field has enabled doctors in different fields of medicine to obtain a precise 3D understanding of 2D images.

Discrimination

Discrimination measurements identify to what extent a model discriminates items of different classes (for example, individuals with disease and without disease). For binary outcomes, the receiver operating characteristic curve or C-statistic could be applied for discrimination measurement.

Feature selection

Feature selection refers to the process of obtaining a subset of variables from an original set of variables according to certain feature selection criteria. The feature selection step precedes the learning step of a prediction model and good feature selection results can improve the learning accuracy, reduce learning time and simplify learning results.

Generalizability

Refers to the accuracy with which a prediction model developed from one study population can be used for the population at large.

Imputation

In machine-learning and statistics, imputation is the process of replacing missing data with substituted values to avoid bias or inaccuracies in the results.

Interpretability

Model interpretability describes the ability of the user to understand the model, which includes understanding the relationships between the input and outcome variables (for example, knowing how the selected input variables contribute to the outcome variable).

Regression models

Regression is the process of identifying the value of a new observation on the basis of a training set of data containing observations for which the category (outcome value) is known. In the field of osteoarthritis, an example could be predicting the probability of disease.

Semi-supervised learning

Semi-supervised learning is typically when only a small amount of data are labelled (that is, have both input and output variables) and a large amount are unlabelled (that is, have only input data); this method falls between unsupervised learning and supervised learning.

Supervised learning

Supervised learning is where you have input variables (x) and an output variable (y) and use an algorithm to learn the mapping function from the input to the output y = f(x).

Training

The training for machine learning involves providing a machine-learning algorithm with training data (input and outcome variables) to learn from. The learning algorithm finds patterns in the training data such that the input parameters correspond to the target. Machine-learning models are applied to do predictions on new data for which the outcome value is not known (for example, to determine to which class the new observation belongs).

Unsupervised learning

In unsupervised learning, only input data (x) exist and there are no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

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https://doi.org/10.1038/s41584-018-0130-5