Weakly supervised medical named entity classification

Overview

Trove

Documentation Status license

Trove is a research framework for building weakly supervised (bio)medical named entity recognition (NER) and other entity attribute classifiers without hand-labeled training data.

The COVID-19 pandemic has underlined the need for faster, more flexible ways of building and sharing state-of-the-art NLP/NLU tools to analyze electronic health records, scientific literature, and social media. Likewise, recent research into language modeling and the dangers of uncurated, "unfathomably" large-scale training data underlines the broader need to approach training set creation itself with more transparency and rigour.

Trove provides tools for combining freely available supervision sources such as medical ontologies from the Unified Medical Language System (UMLS), common text heuristics, and other noisy labeling sources for use as entity labelers in weak supervision frameworks such as Snorkel, FlyingSquid and others. Technical details are available in our manuscript.

Trove has been used as part of several COVID-19 reseach efforts at Stanford.

Getting Started

Tutorials

See tutorials/ for Jupyter notebooks walking through an example NER application.

Installation

Requirements: Python 3.6 or later. We recomend using pip to install

pip install -r requirements.txt

Contributions

We welcome all contributions to the code base! Please submit a pull request and/or start a discussion on GitHub Issues.

Weakly supervised methods for programatically building and maintaining training sets provides new opportunities for the larger community to participate in the creation of important datasets. This is especially exciting in domains such as medicine, where sharing labeled data is often challening due to patient privacy concerns.

Inspired by recent efforts such as HuggingFace's Datasets library, we would love to start a conversation around how to support sharing labelers in service of mantaining an open task library, so that it is easier to create, deploy, and version control weakly supervised models.

Citation

If use Trove in your research, please cite us!

Fries, J.A., Steinberg, E., Khattar, S. et al. Ontology-driven weak supervision for clinical entity classification in electronic health records. Nat Commun 12, 2017 (2021). https://doi-org.stanford.idm.oclc.org/10.1038/s41467-021-22328-4

@article{fries2021trove,
  title={Ontology-driven weak supervision for clinical entity classification in electronic health records},
  author={Fries, Jason A and Steinberg, Ethan and Khattar, Saelig and Fleming, Scott L and Posada, Jose and Callahan, Alison and Shah, Nigam H},
  journal={Nature Communications},
  volume={12},
  number={1},
  year={2021},
  publisher={Nature Publishing Group}
}
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Comments
  • Code in

    Code in "applications" doesn't work; experiments not reproducible

    A lot of the imports in the scripts under the "applications" subfolder fail - for example: from trove.utils import score_umls_ontologies from trove.labelers.norm import lowercase, strip_affixes

    This makes it impossible to reproduce the experiments in the paper

    Could you please share these functions (or the previous version of the repo) to make the code runnable? Thanks a lot in advance!

    opened by tmadl 7
  • Bump joblib from 1.0.1 to 1.2.0

    Bump joblib from 1.0.1 to 1.2.0

    Bumps joblib from 1.0.1 to 1.2.0.

    Changelog

    Sourced from joblib's changelog.

    Release 1.2.0

    • Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

    • Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

    • Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

    • Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

    • Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

    • Vendor loky 3.3.0 which fixes several bugs including:

      • robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

      • avoiding leaking worker processes in case of nested loky parallel calls;

      • reliability spawn the correct number of reusable workers.

    Release 1.1.0

    • Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181

    • Fix joblib.Memory bug with the ignore parameter when the cached function is a decorated function.

    ... (truncated)

    Commits
    • 5991350 Release 1.2.0
    • 3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)
    • cea26ff CI test the future loky-3.3.0 branch (#1338)
    • 8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)
    • 067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)
    • ac4ebd5 MAINT add back pytest warnings plugin (#1337)
    • a23427d Test child raises parent exits cleanly more reliable on macos (#1335)
    • ac09691 [MAINT] various test updates (#1334)
    • 4a314b1 Vendor loky 3.2.0 (#1333)
    • bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • UMLS. init_from_nlm_zip can't decode charmap

    UMLS. init_from_nlm_zip can't decode charmap

    Describe the bug

    I can't install the UMLS as directed by the tutorial notebooks. The UMLS object can't be initialized.

    Steps to reproduce the bug

    I downloaded the relevant zip file from the provided link (https://download.nlm.nih.gov/umls/kss/2020AB/umls-2020AB-metathesaurus.zip) and placed the file in the same directory as the 1_Installing_the_UMLS.ipynb notebook in the tutorials folder. Then I ran the notebook as given in the github.

    Sample code to reproduce the bug

    Expected results

    A clear and concise description of the expected results.

    Actual results

    Specify the actual results or traceback.

    The libraries and python version are all on the pdf attached

    Environment info

    troveDecodeError

    • Python version:

    The libraries and python version are all on the pdf attached

    • PyArrow version:

    The libraries and python version are all on the pdf attached

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  • from spacy.pipeline import SentenceSegmenter

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    Hi, thank you for the interesting piece of work!

    When I tried to preprocess my data, I encountered this error: ImportError: cannot import name 'SentenceSegmenter' from 'spacy.pipeline' when running parse.py. I checked for spacy documentation and it doesn't have the SentenceSegmenter feature. May I get your advice on how to solve this issue?

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