DeepCompartmentModels.jl

A package for fitting Deep Compartment Models in julia.

Most of the basic functionality for fitting deep compartment models has been implemented. If you have any suggestions to improve the package, please do not hesitate to open an issue or submit a pull request.

Introduction

Deep Compartment Models is novel deep learning based modeling framework for fitting machine learning models to time-series data in the medical domain. The aim of these models is to provide insights for the personalization of treatment of patients. The package aims to combine techinques from the field of machine learning and pharmacometrics in order to produce more reliable models.

A main problem with using machine learning as-is for this purpose is that there is a large amount of heterogeneity in measurement timing or treatment intervations between patients. In standard machine learning based approaches, we have to supply information such as the time points of interest or the administered dose directly as inputs to the model, while we are uncertain how the algorithm will treat them. Generally, we thus see that such information is interpreted incorrectly, and thus raises questions regarding the reliablility of the predictions. Instead, by using a system of differential equations, we can explicitly handle such information. The classic deep compartment model structure uses compartment models to constrain the solution to follow certain expectations about drug kinetics and dynamics. Aside from improving model reliability, this also reduces the need for large data sets as we can supply prior knowledge about drug dynamics to the model a priori.

Installation

Installing Julia.

Most pharmacometricians are used to programming in R, and likely do not yet have julia installed. You can download the appropriate julia installer here.

Installing the DeepCompartmentModels package

After installing julia run the julia command in an open command line or bash window to launch a julia REPL. Enter the following commands:

julia> ]
pkg> add DeepCompartmentModels

# or 

julia> using Pkg
julia> Pkg.add("DeepCompartmentModels")

Fitting a model

Deep compartment models consist of a neural network and a system of differential equations. Lux is a machine learning library that aids in definiting complex neural network architectures.

import Optimisers
import CSV

using DataFrames
using DeepCompartmentModels

df = DataFrame(CSV.File("my_dataset.csv"))

population = load(df, [:WEIGHT, :AGE])

ann = Chain(
    # Our data set contains two covariates, which we feed into a hidden layer with 16 neurons
    Dense(2, 16, relu), 
    Dense(16, 4, softplus), # Our differential equation has four parameters
)

# DeepCompartmentModels already exports some compartmental structures including two_comp!
model = DCM(two_comp!, ann, 2) 

fit!(model, population, Optimisers.Adam(), 500) # optimize neural network for 500 epochs

predict(model, population[1]) # predict the concentration for the first individual in the population.