Continual Learning of Electronic Health Records (EHR).

Related tags

Deep Learningehrcontinual-learningdphil
Overview

arXiv License: MIT

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

  1. Clone this repo to your local machine.
  2. Request access to MIMIC-III and eICU-CRD.1
  3. Download the preprocessed datasets to the /data subfolder.
  4. (Recommended) Create and activate a new virtual environment: 
    python3 -m venv .venv --upgrade-deps
  5. 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}
}

Python versions

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
Comments
  • Change experience to class balanced replay

    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

    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

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    You can disable automated security fix PRs for this repo from the Security Alerts page.

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

    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

    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

    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

    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

    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 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
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Data Scientist @publichealthengland
Jacob
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