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--- |
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language: en |
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thumbnail: https://coronacentral.ai/logo-with-name.png?1 |
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tags: |
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- coronavirus |
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- covid |
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- bionlp |
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datasets: |
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- cord19 |
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- pubmed |
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license: mit |
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widget: |
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- 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." |
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- text: "Lifestyle and mental health disruptions during COVID-19." |
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- text: "More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis" |
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--- |
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# CoronaCentral BERT Model for Topic / Article Type Classification |
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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. |
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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. |
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## Usage |
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Below are two Google Colab notebooks with example usage of this sequence classification model using HuggingFace transformers and KTrain. |
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- [HuggingFace example on Google Colab](https://colab.research.google.com/drive/1cBNgKd4o6FNWwjKXXQQsC_SaX1kOXDa4?usp=sharing) |
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- [KTrain example on Google Colab](https://colab.research.google.com/drive/1h7oJa2NDjnBEoox0D5vwXrxiCHj3B1kU?usp=sharing) |
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## Training Data |
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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). |
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### List of Article Types |
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- Comment/Editorial |
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- Meta-analysis |
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- News |
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- Review |
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### List of Topics |
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- Clinical Reports |
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- Communication |
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- Contact Tracing |
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- Diagnostics |
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- Drug Targets |
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- Education |
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- Effect on Medical Specialties |
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- Forecasting & Modelling |
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- Health Policy |
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- Healthcare Workers |
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- Imaging |
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- Immunology |
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- Inequality |
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- Infection Reports |
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- Long Haul |
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- Medical Devices |
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- Misinformation |
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- Model Systems & Tools |
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- Molecular Biology |
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- Non-human |
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- Non-medical |
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- Pediatrics |
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- Prevalence |
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- Prevention |
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- Psychology |
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- Recommendations |
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- Risk Factors |
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- Surveillance |
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- Therapeutics |
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- Transmission |
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- Vaccines |
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