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Targeted-BEHRT

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# Targeted BEHRT
Repository for publication: Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records<br/>
IEEE Transactions on Neural Networks and Learning Systems; Special Issue on Causality<br/>
https://ieeexplore.ieee.org/document/9804397/<br/>
DOI: 10.1109/TNNLS.2022.3183864.<br/>



How to use:<br/>
In "examples" folder, run the "run_TBEHRT.ipynb" file. A test.csv file is provided to test/play and demonstrate how the vocabulary/year/age/etc function (please read full paper linked above for further methodological details). <br/>
Furthermoree, in the examples folder to run the CV-TMLE estimator, run the "CVTMLE_example.ipynb" file. A host of fake fold data is provided to test/play and demonstrate how the CV-TMLE algorithm works (please read methods publication of CV-TMLE for further details). <br/>

The files in the "src" folder contain model and data handling packages in addition to other necessary VAE relevant files and helper functions.

Requirements:<br/>
torch >1.6.0<br/>
numpy 1.19.2<br/>
sklearn 0.23.2<br/>
pandas 1.1.3<br/>
<br/>

	a/README.md		b/README.md
1	# Targeted BEHRT	1	# Targeted BEHRT
2	Repository for publication: Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records<br/>	2	Repository for publication: Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records<br/>
3	IEEE Transactions on Neural Networks and Learning Systems; Special Issue on Causality<br/>	3	IEEE Transactions on Neural Networks and Learning Systems; Special Issue on Causality<br/>
4	https://ieeexplore.ieee.org/document/9804397/<br/>	4	https://ieeexplore.ieee.org/document/9804397/<br/>
5	DOI: 10.1109/TNNLS.2022.3183864.<br/>	5	DOI: 10.1109/TNNLS.2022.3183864.<br/>
6		6
7	![Screenshot](screenshot.png)
8
9	How to use:<br/>	7	How to use:<br/>
10	In "examples" folder, run the "run_TBEHRT.ipynb" file. A test.csv file is provided to test/play and demonstrate how the vocabulary/year/age/etc function (please read full paper linked above for further methodological details). <br/>	8	In "examples" folder, run the "run_TBEHRT.ipynb" file. A test.csv file is provided to test/play and demonstrate how the vocabulary/year/age/etc function (please read full paper linked above for further methodological details). <br/>
11	Furthermoree, in the examples folder to run the CV-TMLE estimator, run the "CVTMLE_example.ipynb" file. A host of fake fold data is provided to test/play and demonstrate how the CV-TMLE algorithm works (please read methods publication of CV-TMLE for further details). <br/>	9	Furthermoree, in the examples folder to run the CV-TMLE estimator, run the "CVTMLE_example.ipynb" file. A host of fake fold data is provided to test/play and demonstrate how the CV-TMLE algorithm works (please read methods publication of CV-TMLE for further details). <br/>
12		10
13	The files in the "src" folder contain model and data handling packages in addition to other necessary VAE relevant files and helper functions.	11	The files in the "src" folder contain model and data handling packages in addition to other necessary VAE relevant files and helper functions.
14		12
15	Requirements:<br/>	13	Requirements:<br/>
16	torch >1.6.0<br/>	14	torch >1.6.0<br/>
17	numpy 1.19.2<br/>	15	numpy 1.19.2<br/>
18	sklearn 0.23.2<br/>	16	sklearn 0.23.2<br/>
19	pandas 1.1.3<br/>	17	pandas 1.1.3<br/>
20	<br/>	18	<br/>