This repo contains a C++ implementation of our paper, "Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data" [PDF].
@inproceedings{hawkes,
title={Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data},
author={Bao, Yujia and Kuang, Zhaobin and Peissig, Peggy and Page, David and Willett, Rebecca },
booktitle={Machine Learning for Healthcare Conference},
year={2017} }
Each row in the data file represent the trajectory of one patient, which is a sequence of ordered time-event pairs separated by whitespace:
t_1 m_1 t_2 m_2 ... t_n m_n
where 0 <= t_1 <= ... <= t_n and m_i in {1,...,numOfVariables}.
We assume {1,...,numOfOutcomes} to be the indices for the adverse outcomes and {numOfOutcomes+1, ..., numOfVariables} to be the indices for the drugs of interest.
The Marshfield Clinic EHR data is not publicly available due to patients' privacy. We provide a synthetic data generated from a Poisson autoregressive model for illustration.
- Compile
g++ --std=c++14 -pthread Hawkes.cpp -o Hawkes.out
- Example run
./Hawkes.out -f path_to_data -k num_of_kernels -w length_of_windows -l lasso_regularizations
The MIT License (MIT)
Copyright (c) 2017 Yujia Bao