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+# Continual Learning of Longitudinal Health Records
+
+[![arXiv](https://img.shields.io/badge/arXiv-2112.11944-b31b1b.svg)](https://arxiv.org/abs/2112.11944) [![PyTorch](https://img.shields.io/badge/​-PyTorch-%23EE4C2C.svg?style=flat&logo=PyTorch)](https://pytorch.org/) [![License: GPLv3](https://img.shields.io/badge/license-GPLv3-green.svg)](https://opensource.org/licenses/gpl-3-0) [![Python](https://badges.aleen42.com/src/python.svg)](https://www.python.org/) ![uv](https://img.shields.io/badge/%E2%80%8B-uv-%23A100FF.svg?style=flat&logo=uv&logoColor=A100FF) 
+
+Repo for reproducing the experiments in [*Continual Learning of Longitudinal Health Records*](https://arxiv.org/abs/2112.11944) (2021). Release [v0.1](releases/v0.1) of the project corresponds to published results.
+
+Experiments evaluate various continual learning strategies on standard ICU predictive tasks exhibiting covariate shift. Task outcomes are binary, and input data are multi-modal time-series from patient ICU admissions.
+
+## Setup
+
+1. Clone this repo locally.
+2. Request access to [MIMIC-III](https://www.physionet.org/content/mimiciii/1.4/) and [eICU-CRD](https://www.physionet.org/content/eicu-crd/2.0/).<sup>1</sup>
+3. Download the [preprocessed datasets](https://physionet.org/files/mimic-eicu-fiddle-feature/1.0.0/) to the `/data` directory.
+
+## Results
+
+To reproduce main results:
+
+```zsh
+uv run main.py --train
+```
+
+Figures will be saved to `/results/figs`. Instructions to reproduce supplementary experiments can be found [here](/results/README.md). Bespoke experiments can be specified with appropriate flags e.g:
+
+```posh
+uv run main.py --domain_shift "hospital" --outcome "mortality_48h" --models "CNN" --strategies "EWC" "Replay" --validate --train
+```
+
+A complete list of available options can be found [here](/config/README.md) or with `uv run main.py --help`.
+
+## Citation
+
+If you use any of this code in your work, please reference us:
+
+```latex
+@misc{armstrong2021continual,
+      title={Continual learning of longitudinal health records}, 
+      author={J. Armstrong and D. Clifton},
+      year={2021},
+      eprint={2112.11944},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+
+---
+
+### Stack
+
+For standardisation of ICU predictive task definitions, feature pre-processing, and Continual Learning method implementations, we use the following tools:
+
+| Tool                        | Source               |
+|-----------------------------|----------------------|
+|ICU Data                     | [MIMIC-III](https://www.physionet.org/content/mimiciii/1.4/)<br> [eICU-CRD](https://www.physionet.org/content/eicu-crd/2.0/) |
+|Data preprocessing / task definition | [FIDDLE](https://www.physionet.org/content/mimic-eicu-fiddle-feature/1.0.0/) |
+|Continual Learning strategies| [Avalanche](https://avalanche.continualai.org/)
+
+> [!NOTE]
+> Temporal Domain Incremental learning experiments require linkage with original MIMIC-III dataset. Requires downloading `ADMISSIONS.csv` from [MIMIC-III](https://physionet.org/content/mimiciii/1.4/) to the `/data/mimic3/` folder.