Computational patient

This repository contains the python code required to reproduce the experiments presented in "Barbiero, P. and Liò, P. (2020). The Computational Patient has Diabetes and a COVID".

Description

Medicine is moving from reacting to a disease to prepare personalised and precision paths to well being. The complex and multi level pathophysiological patterns of most diseases require a systemic medicine approach and are challenging current medical therapies.

Here we present a Digital patient model that integrates, refine and extend recent specific mechanistic or phenomenological models ofcardiovascular [1], RAS [2] and diabetic [3] processes. Our aim is twofold: analyse the modularity and composability of the models-building blocks of the Digital patient and to study the dynamical properties of well-being and disease states in a broader functional context. We present results from a number of experiments among which we characterise the dynamical impact of covid-19 and T2D diabetes on cardiovascular and inflammaging conditions. We tested these experiments under exercise and meals and drug regimen.

Common clinical parameters such as diastolic and systolic blood pressure, heart patterns, blood cell counts are usually evaluated as averages. Little importance is given to higher moments such as variances during the day or during a longer interval of time. The lack of continuous measures for most of the quantities has generated a medical practice that disregard of unobserved or partially observed data. Our composable model reveals interesting patterns, particularly fluctuations in blood pressure, particularly when the diabetic model is coupled with the RAS and the cardiovascular models under COVID acute infections.

References

[1] Neal, M. L., & Bassingthwaighte, J. B. (2007). Subject-specific model estimation of cardiac output and blood volume during hemorrhage. Cardiovascular engineering, 7(3), 97-120.

[2] Pilvankar, M. R., Yong, H. L., & Ford Versypt, A. N. (2019). A Glucose-Dependent Pharmacokinetic/Pharmacodynamic Model of ACE Inhibition in Kidney Cells. Processes, 7(3), 131.

[3] Topp, B., Promislow, K., Devries, G., Miura, R. M., & Finegood, D. T. (2000). A model of b-cell mass, insulin, and glucose kinetics: pathways to diabetes. Journal of theoretical biology, 206(4), 605.

Quick start

You can install the software along with all its dependencies from GitHub:

$ git clone https://github.com/pietrobarbiero/computational-patient.git
$ cd ./computational-patient
$ pip install -r requirements.txt .

After having installed all the requirements you can run the example script ./examples/aging.py for running experiments. The results will be saved under the directory ./examples/data/.

Once the results have been saved, you can make some plots by running the script ./examples/make_plots.py.

Contributing

The project (./msmodel directory) has the following structure:

• the pk directory is used to specify the equations of the Pharmacokinetic model
• the pd directory is used to specify the equations of the Pharmacodynamic model
• the circulation directory is used to specify the equations of the circulatory system
• the ode directory is used to specify the ODE systems

The _config.py file is used to parse command line arguments.

The script _msmodel.py contains the main interface function to run the ODE systems.

Source

The source code and minimal working examples can be found on GitHub.

Licence

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and limitations under the License.