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

    Dependabot compatibility score

    Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

    Dependabot commands and options

    You can trigger Dependabot actions by commenting on this PR:

    • @dependabot rebase will rebase this PR
    • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
    • @dependabot merge will merge this PR after your CI passes on it
    • @dependabot squash and merge will squash and merge this PR after your CI passes on it
    • @dependabot cancel merge will cancel a previously requested merge and block automerging
    • @dependabot reopen will reopen this PR if it is closed
    • @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
    • @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
    • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
    • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
    • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
    • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
    • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

    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
Releases(v0.1)
Owner
Jacob
Data Scientist @publichealthengland
Jacob
GitHub Repository
A port of muP to JAX/Haiku

MUP for Haiku This is a (very preliminary) port of Yang and Hu et al.'s μP repo to Haiku and JAX. It's not feature complete, and I'm very open to sugg

18 Dec 30, 2022
Data loaders and abstractions for text and NLP

torchtext This repository consists of: torchtext.datasets: The raw text iterators for common NLP datasets torchtext.data: Some basic NLP building bloc

3.2k Jan 08, 2023
Sound Event Detection with FilterAugment

Sound Event Detection with FilterAugment Official implementation of Heavily Augmented Sound Event Detection utilizing Weak Predictions (DCASE2021 Chal

43 Aug 28, 2022
Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework via Self-Supervised Multi-Task Learning. Code will be available soon.

Official-PyTorch-Implementation-of-TransMEF Official PyTorch implementation of our AAAI22 paper: TransMEF: A Transformer-Based Multi-Exposure Image Fu

117 Dec 27, 2022
Train Yolov4 using NBX-Jobs

yolov4-trainer-nbox Train Yolov4 using NBX-Jobs. Use the powerfull functionality available in nbox-SDK repo to train a tiny-Yolo v4 model on Pascal VO

Yash Bonde 1 Jan 12, 2022
A modular PyTorch library for optical flow estimation using neural networks

A modular PyTorch library for optical flow estimation using neural networks

neu-vig 113 Dec 20, 2022
Natural Intelligence is still a pretty good idea.

Human Learn Machine Learning models should play by the rules, literally. Project Goal Back in the old days, it was common to write rule-based systems.

vincent d warmerdam 641 Dec 26, 2022
This is the official implementation of our proposed SwinMR

SwinMR This is the official implementation of our proposed SwinMR: Swin Transformer for Fast MRI Please cite: @article{huang2022swin, title={Swi

A Yang Lab (led by Dr Guang Yang) 27 Nov 17, 2022
2021-AIAC-QQ-Browser-Hyperparameter-Optimization-Rank6

2021-AIAC-QQ-Browser-Hyperparameter-Optimization-Rank6

Aigege 8 Mar 31, 2022
NeRViS: Neural Re-rendering for Full-frame Video Stabilization

Neural Re-rendering for Full-frame Video Stabilization

Yu-Lun Liu 9 Jun 17, 2022
Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation", Haoxiang Wang, Han Zhao, Bo Li.

Bridging Multi-Task Learning and Meta-Learning Code for the ICML 2021 paper "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Trainin

AI Secure 57 Dec 15, 2022
This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".

On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness This repository provides the code for the paper On Interaction B

Meta Research 33 Dec 08, 2022
Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

AmbientGAN: Generative models from lossy measurements This repository provides code to reproduce results from the paper AmbientGAN: Generative models

Ashish Bora 87 Oct 19, 2022
Semi-Supervised Learning for Fine-Grained Classification

Semi-Supervised Learning for Fine-Grained Classification This repo contains the code of: A Realistic Evaluation of Semi-Supervised Learning for Fine-G

25 Nov 08, 2022
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
PyTorch implementation of Glow

glow-pytorch PyTorch implementation of Glow, Generative Flow with Invertible 1x1 Convolutions (https://arxiv.org/abs/1807.03039) Usage: python train.p

Kim Seonghyeon 433 Dec 27, 2022
PyTorch implementation of MulMON

MulMON This repository contains a PyTorch implementation of the paper: Learning Object-Centric Representations of Multi-object Scenes from Multiple Vi

NanboLi 16 Nov 03, 2022
IDA file loader for UF2, created for the DEFCON 29 hardware badge

UF2 Loader for IDA The DEFCON 29 badge uses the UF2 bootloader, which conveniently allows you to dump and flash the firmware over USB as a mass storag

Kevin Colley 6 Feb 08, 2022
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022
Semantic Segmentation Architectures Implemented in PyTorch

pytorch-semseg Semantic Segmentation Algorithms Implemented in PyTorch This repository aims at mirroring popular semantic segmentation architectures i

Meet Shah 3.3k Dec 29, 2022