Personalised PHQ-9 Model

This repository hosts the code related to the paper:

Abdulhussein, Z., Scazufca, M., & Van de Ven, P. (2026).
Personalised PHQ-9 test length using probability density estimation based on conditional probability and K-Nearest Neighbours.
Internet Interventions. https://doi.org/10.1016/j.invent.2026.100919

The model is implemented in Python in the file dynamic_model.py.
The file example.ipynb is a Jupyter notebook that demonstrates how to use the model with a simple example.

If you use this code in academic work, please cite both the software and the associated paper.

Abdulhussein, Z., & Van de Ven, P. (2026). Personalised PHQ-9 Model (v1.0.0) [Source code]. Zenodo.

https://doi.org/10.5281/zenodo.18623952

Abdulhussein, Z., Scazufca, M., & Van de Ven, P. (2026). Personalised PHQ-9 test length using probability density estimation based on conditional probability and K-Nearest Neighbours. Internet Interventions.
https://doi.org/10.1016/j.invent.2026.100919

BibTeX

@software{abdulhussein2026phq9,
  author = {Abdulhussein, Zahraa and Van de Ven, Pepijn},
  title = {Personalised PHQ-9 Model},
  version = {v1.0.0},
  year = {2026},
  doi = {10.5281/zenodo.18623953},
  url = {https://github.com/zahraa-m/Personalised-PHQ-9-Model}
}

@article{abdulhussein2026phq9paper,
  author = {Abdulhussein, Zahraa and Scazufca, Marcia and Van de Ven, Pepijn},
  title = {Personalised PHQ-9 test length using probability density estimation based on conditional probability and K-Nearest Neighbours},
  journal = {Internet Interventions},
  year = {2026},
  doi = {10.1016/j.invent.2026.100919}
}