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license: mit
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# 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. |