Clinical-NER-MedMentions-GatorTronS

Model Card for Model longluu/Clinical-NER-MedMentions-GatorTronS

The model is an NER LLM algorithm that can classify each word in a text into different clinical categories.

Model Details

Model Description

The base pretrained model is GatorTronS which was trained on billions of words in various clinical texts (https://huggingface.co/UFNLP/gatortronS). Then using the MedMentions dataset (https://arxiv.org/pdf/1902.09476v1.pdf), I fine-tuned the model for NER task in which the model can classify each word in a text into different clinical categories. The category system is a simplified version of UMLS concept system and consists of 21 categories: "['Living Beings', 'Virus']", "['Living Beings', 'Bacterium']", "['Anatomy', 'Anatomical Structure']", "['Anatomy', 'Body System']", "['Anatomy', 'Body Substance']", "['Disorders', 'Finding']", "['Disorders', 'Injury or Poisoning']", "['Phenomena', 'Biologic Function']", "['Procedures', 'Health Care Activity']", "['Procedures', 'Research Activity']", "['Devices', 'Medical Device']", "['Concepts & Ideas', 'Spatial Concept']", "['Occupations', 'Biomedical Occupation or Discipline']", "['Organizations', 'Organization']", "['Living Beings', 'Professional or Occupational Group']", "['Living Beings', 'Population Group']", "['Chemicals & Drugs', 'Chemical']", "['Objects', 'Food']", "['Concepts & Ideas', 'Intellectual Product']", "['Physiology', 'Clinical Attribute']", "['Living Beings', 'Eukaryote']", 'None'

Model Sources [optional]

The github code associated with the model can be found here: https://github.com/longluu/LLM-NER-clinical-text.

Training Details

Training Data

The MedMentions dataset contain 4,392 abstracts released in PubMed®1 between January 2016 and January 2017. The abstracts were manually annotated for biomedical concepts. Details are provided in https://arxiv.org/pdf/1902.09476v1.pdf and data is in https://github.com/chanzuckerberg/MedMentions.

Training Hyperparameters

The hyperparameters are --batch_size 6 --num_train_epochs 6 --learning_rate 5e-5 --weight_decay 0.01

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was trained and validated on train and validation sets. Then it was tested on a separate test set. Note that some concepts in the test set were not available in the train and validatin sets.

Metrics

Here we use several metrics for classification tasks including macro-average F1, precision, recall and Matthew correlation.

Results

{'f1': 0.6282171983322534, 'precision': 0.6449102548010544, 'recall': 0.6123665141113653}

Model Card Contact

Feel free to reach out to me at [email protected] if you have any question or suggestion.