Continual Learning of Electronic Health Records (EHR).

Last update: Oct 21, 2022

Overview

Continual Learning of Longitudinal Health Records

Repo for reproducing the experiments in Continual Learning of Longitudinal Health Records (2021). Release 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

Clone this repo to your local machine.
Request access to MIMIC-III and eICU-CRD.¹
Download the preprocessed datasets to the /data subfolder.
(Recommended) Create and activate a new virtual environment:
```
python3 -m venv .venv --upgrade-deps
```

Install dependencies:

pip install -U wheel buildtools
pip install -r requirements.txt

Results

To reproduce main results:

python3 main.py --train

Figures will be saved to /results/figs. Instructions to reproduce supplementary experiments can be found here. Bespoke experiments can be specified with appropriate flags e.g:

python3 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 or with python3 main.py --help.

Citation

If you use any of this code in your work, please reference us:

@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}
}

^{Notes
Note that Temporal Domain Incremental learning experiments require linkage with original MIMIC-III dataset. Requires downloading ADMISSIONS.csv from MIMIC-III to the /data/mimic3/ folder.

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
eICU-CRD

Data preprocessing / task definition
FIDDLE

Continual Learning strategies
Avalanche}

Tool	Source
ICU Data	MIMIC-III eICU-CRD
Data preprocessing / task definition	FIDDLE
Continual Learning strategies	Avalanche

Comments

Change experience to class balanced replay

Have manually edited the replay definition for now. Will need to update avalanche and do change based on training.storage_policy.

May also need to change memory buffer to n_tasks * buffer (since GEM etc use this number for experience-wise buffer sizes).

opened by iacobo 1
Bump numpy from 1.20.3 to 1.22.0
Bumps numpy from 1.20.3 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0
Add Naive with no regularization?

Maybe add naive with no regularization? I.e. no dropout etc, to enable clearer ablation testing of naive fine tuning and inherent regularization mechanisms vs explicit CL strategy.

opened by iacobo 0
CNN fails with kernel_size 5 or 7
Getting the following error (on GPU) with CNN runs with kernel_size in [5,7]:

RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`

https://stackoverflow.com/questions/66600362/runtimeerror-cuda-error-cublas-status-execution-failed-when-calling-cublassge?answertab=votes#tab-top
opened by iacobo 0
Add early stopping to avoid over-large number of epochs for diff models

MLP / LSTM take shorter time to train than CNN / Transformer. Add early stopping to avoid overtraining, saturating.

Change strategy to base strategy inheriting from strat and earlystopping plugin.

opened by iacobo 0
Correct code for ROC AUC and AUPRC

Cannot average metrics over minibatches as is done for other metrics, since they depend on threshold. Need to calculate over all. Check e.g. MeanScore for inspiration on metric definition.

opened by iacobo 0

Need to add code for further experiments

plotting.plot_demographics()

# Secondary experiments:
########################
# Sensitivity to sequence length (4hr vs 12hr)
# Sensitivity to replay size Naive -> replay -> Cumulative
# Sensitivity to hyperparams of reg methods (Tune hyperparams over increasing number of tasks?)
# Sensitivity to number of variables (full vs Vitals only e.g.)
# Sensitivity to size of domains - e.g. white ethnicity much larger than all other groups, affect of order of sequence

opened by iacobo 1

Ray Tune warnings
Ray Tune produces the following warnings:

INFO registry.py:66 -- Detected unknown callable for trainable. Converting to class. WARNING experiment.py:295 -- No name detected on trainable. Using DEFAULT.

Non-fatal, but it's annoying to have these messages bloating the console output.
raytune
opened by iacobo 2

Releases(v0.1)

v0.1(Dec 23, 2021)

Code version corresponding to preliminary results on arXiv.
Source code(tar.gz)
Source code(zip)

Owner

Jacob

Data Scientist @publichealthengland

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