Pytorch implementation of Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence, CHIL 2022.

This model (CIM) corrects misspellings with a char-based language model and a corruption model (edit distance). The model is being pre-trained and evaluated on clinical corpus and datasets. Please see the paper for more detailed explanation.

• Python 3.8 and packages in requirements.txt
• The MIMIC-III dataset (v1.4): PhysioNet link
• BlueBERT: GitHub link
• The SPECIALIST Lexicon of UMLS: LSG website
• English dictionary (DWYL): GitHub link

$ git clone --recursive https://github.com/dalgu90/cim-misspelling.git

1. Download the MIMIC-III dataset from PhysioNet, especially NOTEEVENTS.csv and put under data/mimic3

2. Download LRWD and prevariants of the SPECIALIST Lexicon from the LSG website (2018AB version) and put under data/umls.

3. Download the English dictionary english.txt from here (commit 7cb484d) and put under data/english_words.

4. Run scripts/build_vocab_corpus.ipynb to build the dictionary and split the MIMIC-III notes into files.

5. Run the Jupyter notebook for the dataset that you want to download/pre-process:

   • MIMIC-III misspelling dataset, or ClinSpell (Fivez et al., 2017): scripts/preprocess_clinspell.ipynb
   • CSpell dataset (Lu et al., 2019): scripts/preprocess_cspell.ipynb
   • Synthetic misspelling dataset from the MIMIC-III: scripts/synthetic_dataset.ipynb

6. Download the BlueBERT model from here under bert/ncbi_bert_{base|large}.

   • For CIM-Base, please download "BlueBERT-Base, Uncased, PubMed+MIMIC-III"
   • For CIM-Large, please download "BlueBERT-Large, Uncased, PubMed+MIMIC-III"

Please run pretrain_cim_base.sh (CIM-Base) or pretrain_cim_large.sh(CIM-Large) and to pretrain the character langauge model of CIM. The pre-training will evaluate the LM periodically by correcting synthetic misspells generated from the MIMIC-III data. You may need 2~4 GPUs (XXGB+ GPU memory for CIM-Base and YYGB+ for CIM-Large) to pre-train with the batch size 256. There are several options you may want to configure:

• num_gpus: number of GPUs
• batch_size: batch size
• training_step: total number of steps to train
• init_ckpt/init_step: the checkpoint file/steps to resume pretraining
• num_beams: beam search width for evaluation
• mimic_csv_dir: directory of the MIMIC-III csv splits
• bert_dir: directory of the BlueBERT files

You can also download the pre-trained LMs and put under model/ (e.g. the CIM-base checkpoint is placed as model/cim_base/ckpt-475000.pkl):

• CIM-Base (12-layer)
• CIM-Large (24-layer)

Please specify the dataset dir and the file to evaluate in the evaluation script (eval_cim_base.sh or eval_cim_large.sh), and run the script.
You may want to set init_step to specify the checkpoint you want to load

@InProceedings{juyong2022context,
  title = {Context-Sensitive Spelling Correction of Clinical Text via Conditional Independence},
  author = {Kim, Juyong and Weiss, Jeremy C and Ravikumar, Pradeep},
  booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
  pages = {234--247},
  year = {2022},
  volume = {174},
  series = {Proceedings of Machine Learning Research},
  month = {07--08 Apr},
  publisher = {PMLR}
}