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. Citation If you use this code in academic work, please cite both the software and the associated paper. Software citation Abdulhussein, Z., & Van de Ven, P. (2026). Personalised PHQ-9 Model (v1.0.0) [Source code]. Zenodo. https://doi.org/10.5281/zenodo.18623952 Paper citation 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}}

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