Coder Social home page Coder Social logo

sdi-detection's Introduction

SUPP.AI: detecting supplement-drug interactions

This repository contains data used to detect dietary supplement interactions from scientific articles on SUPP.AI. We train the RoBERTa-DDI model using drug-drug interaction data from the DDI-2013 and NLM-DailyMed datasets. We use this model to extract evidence sentences for supplement interactions from 22M articles in Semantic Scholar.

The resulting interactions are available for search at SUPP.AI.

Extracted evidence is available for bulk download here.

See our arXiv preprint for implementation and data details.

Please address feedback to lucyw [at] allenai [dot] org.

RoBERTa-DDI Model

RoBERTa-DDI uses pre-trained representations from the RoBERTa language model and fine-tunes these representations on DDI classification data. The model is implemented using AllenNLP.

Training data

Training data is derived from the DDI-2013 and NLM-DailyMed datasets. Train/test splits are preserved from the Merged PDDI data release. Development sets are split from the training set for each of the two datasets. Additional pre-processing is performed to create pairwise combinations of entities from each sentence.

Train/Dev/Test splits are available in training_data/.

Supplement interaction evaluation data

A set of 500 sentences are manually labeled for the presence or absence of a supplement-related interaction. These labels are provided in sdi_eval.tsv.

Performance of RoBERTa-DDI on the DDI and SDI test sets is given below:

Test set Precision Recall F1-score
Drugs (DDI-2013) 0.90 0.87 0.88
Drugs (NLM-DailyMed) 0.83 0.85 0.84
SDI-eval 0.82 0.58 0.68

UMLS CUI clusters

We leverage UMLS Metathesaurus identifiers (CUIs) to identify supplement and drug entities. We perform filtering and clustering to create a list of supplement and drug identifiers which we surface on SUPP.AI. These identifier clusters are available at cui_clusters.json.

Citation

If using this data, please cite our arXiv preprint:

@misc{Wang2019ExtractingEO,
  title={Extracting evidence of supplement-drug interactions from literature},
  author={Lucy Lu Wang and Oyvind Tafjord and Sarthak Jain and Arman Cohan and Sam Skjonsberg and Carissa Schoenick and Nick Botner and Waleed Ammar},
  archivePrefix={ArXiv},
  primaryClass={cs.CL},
  year={2019},
  eprint={1909.08135}
}

sdi-detection's People

Contributors

lucylw avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.