Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Abstract

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Figure 1

Schematic visualization of a decision tree, an ensemble of several decision trees (the random forest), and the pharmacometrics modeling approach. [Colour figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Illustration of the problem of underfitting and overfitting based on a polynomial regression. Different models (red curves) are fit to a set of noisy samples (blue points) from the function y = sin(5x) (green curve). Each subplot present the results from a regression model of degree n (i.e., a model f(x) = β₀ + β₁ x ₁ + β₂  x ₂ + … + β_nx_n with scalar regression coefficients β₀, …, β_n). Underfitting (left subplot) occurs when the model has too little capacity to capture the complexity of the data, whereas overfitting (right subplot) fits the data points well, but is unlikely to generalize well to new samples from the underlying function. The central subplot shows a fit that is “just right” in that it closely approximates the true function given a set of samples.

Figure 3

Comparison of the machine learning (ML) classification and pharmacometrics (PMX) modeling approach with respect to dataset preparation, model building, and prediction capability. [Colour figure can be viewed at http://wileyonlinelibrary.com]