Requirement

• python env: 3.8+
• use pip install -r requirements.txt to install dependencies

Models

• we have two models trained for NER and Relation
• Both models base on BERT architecture with different classifiers
• we provide models on Huggingface:
• NER:https://huggingface.co/nvbic/SODA_BERT_NER
• RE:https://huggingface.co/nvbic/SODA_BERT_RE
• contact: zehao.yu@ufl.edu; alexgre@ufl.edu; yonghui.wu@ufl.edu

SDoH_NLPend2end System

• The system aims for extract SDoH information from clinical notes
• We support text format for production and brat format for evaluation
• The system is a two stage pipeline
  • The first stage is to extract SDoH concepts
  • The second stage is to identify relations between extracted concepts

Usage

• download the models and unzip into this project root directory, you should have:
  • ./models/ner_bert
  • ./models/re_bert
• then, cd to the ./scripts directory
• execute pipeline as

bash run_pred.sh -i <input data directory> -c gpu_id

• "input data directory" is the location of the data you annotated (*.txt and *.ann) e.g., ./test_data
• gpu_id is the id where you want to run the program. e.g, 0 - use the GPU with id as 0
• if GPU is not available, try -1 to use CPU which is slow but should work.

Results

• in the main directory (./SDoH_NLPend2end), we will create three directories for outputs
• the first is ./logs which saves all the running logs
• the second is ./temp which saves all the intermediate generated files
• the third is ./results where the eval_results.txt stores the final performance measurement and the rest directories are the e2e outputs