A package for fitting Deep Compartment Models in julia.
Most of the basic functionality for fitting these 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.
The deep compartment model (DCMs) framework is novel deep learning based modeling framework for fitting machine learning (ML) 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 ML 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 interventions between patients. In standard ML-based algorithms, 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 time and other interventions. The standard DCM 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.
We are also working on bringing NeuralODE capabilities to the framework. These
features are available under the HybridDCM type.
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")A DCM consists of a neural network and a system of differential
equations. Lux is a machine learning
library that aids in definiting complex neural network architectures. It is
automatically loaded in the REPL session after running
using DeepCompartmentModels, so you can direclty make use of functions like
Lux.Chain and Lux.Dense.
The DeepCompartmentModels package already exports some compartmental structures including one_comp! and two_comp!. Pull requests adding new compartmental structures are very welcome!
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
)
model = DCM(two_comp!, 2, ann) # passing the number of compartments (2) is necessary here.
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.Whenever you use contents from this package, please help us spread interest by citing the original work on which this package has been based:
Janssen, A., Leebeek, F. W., Cnossen, M. H., Mathôt, R. A., OPTI‐CLOT study group and SYMPHONY consortium, (2022). Deep compartment models: a deep learning approach for the reliable prediction of time‐series data in pharmacokinetic modeling. CPT: Pharmacometrics & Systems Pharmacology, 11(7), 934-945.