| --- |
| language: en |
| thumbnail: https://coronacentral.ai/logo-with-name.png?1 |
| tags: |
| - coronavirus |
| - covid |
| - bionlp |
| datasets: |
| - cord19 |
| - pubmed |
| license: mit |
| widget: |
| - text: "Pre-existing T-cell immunity to SARS-CoV-2 in unexposed healthy controls in Ecuador, as detected with a COVID-19 Interferon-Gamma Release Assay." |
| - text: "Lifestyle and mental health disruptions during COVID-19." |
| - text: "More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis" |
| --- |
| # CoronaCentral BERT Model for Topic / Article Type Classification |
|
|
| This is the topic / article type multi-label classification for the [CoronaCentral website](https://coronacentral.ai). This forms part of the pipeline for downloading and processing coronavirus literature described in the [corona-ml repo](https://github.com/jakelever/corona-ml) with available [step-by-step descriptions](https://github.com/jakelever/corona-ml/blob/master/stepByStep.md). The method is described in the [preprint](https://doi.org/10.1101/2020.12.21.423860) and detailed performance results can be found in the [machine learning details](https://github.com/jakelever/corona-ml/blob/master/machineLearningDetails.md) document. |
|
|
| This model was derived by fine-tuning the [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) model on this coronavirus sequence (document) classification task. |
|
|
| ## Usage |
|
|
| Below are two Google Colab notebooks with example usage of this sequence classification model using HuggingFace transformers and KTrain. |
|
|
| - [HuggingFace example on Google Colab](https://colab.research.google.com/drive/1cBNgKd4o6FNWwjKXXQQsC_SaX1kOXDa4?usp=sharing) |
| - [KTrain example on Google Colab](https://colab.research.google.com/drive/1h7oJa2NDjnBEoox0D5vwXrxiCHj3B1kU?usp=sharing) |
|
|
| ## Training Data |
|
|
| The model is trained on ~3200 manually-curated articles sampled at various stages during the coronavirus pandemic. The code for training is available in the [category\_prediction](https://github.com/jakelever/corona-ml/tree/master/category_prediction) directory of the main Github Repo. The data is available in the [annotated_documents.json.gz](https://github.com/jakelever/corona-ml/blob/master/category_prediction/annotated_documents.json.gz) file. |
|
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| ## Inputs and Outputs |
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| The model takes in a tokenized title and abstract (combined into a single string and separated by a new line). The outputs are topics and article types, broadly called categories in the pipeline code. The types are listed below. Some others are managed by hand-coded rules described in the [step-by-step descriptions](https://github.com/jakelever/corona-ml/blob/master/stepByStep.md). |
|
|
| ### List of Article Types |
|
|
| - Comment/Editorial |
| - Meta-analysis |
| - News |
| - Review |
|
|
| ### List of Topics |
|
|
| - Clinical Reports |
| - Communication |
| - Contact Tracing |
| - Diagnostics |
| - Drug Targets |
| - Education |
| - Effect on Medical Specialties |
| - Forecasting & Modelling |
| - Health Policy |
| - Healthcare Workers |
| - Imaging |
| - Immunology |
| - Inequality |
| - Infection Reports |
| - Long Haul |
| - Medical Devices |
| - Misinformation |
| - Model Systems & Tools |
| - Molecular Biology |
| - Non-human |
| - Non-medical |
| - Pediatrics |
| - Prevalence |
| - Prevention |
| - Psychology |
| - Recommendations |
| - Risk Factors |
| - Surveillance |
| - Therapeutics |
| - Transmission |
| - Vaccines |
|
|
