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"Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data" MLHC 2017

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Hawkes-ADR

A C++ implementation of our paper, "Hawkes Process Modeling of Adverse Drug Reactions with Longitudinal Observational Data"

@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} }

Data format

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 $0\leq t_1\leq\cdots\leq t_n$ and $m_i\in{1,\ldots,\text{numOfVariables}}$. We assume ${1,\ldots,\text{numOfOutcomes}}$ are the indices for the adverse outcomes and ${\text{numOfOutcomes}+1,\ldots,\text{numOfVariables}}$ are the idices for the drugs of interest.

Todo:

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Todo:

  • Data format
  • Running on the synthetic example

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