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Here we document the scripts we used for all experiments in our paper:

Main Figures

  • Fig 2b,c - Exp 1,2,3

  • Fig 3a - Exp 5

  • Fig 3b - Exp 2, 3

  • Fig 3c,d - Exp 1,2

  • Fig 4a - Exp 6, Exp 5

  • Fig 4b - annotations from residents

Figure plotting script and data for plotting can be found at visualization/main_figures

Extended Data Figures

  • ED Fig 1a - Exp 5

  • ED Fig 1b - Exp 2

  • ED Fig 2a - Exp 3

  • ED Fig 2b - Exp 7

  • ED Fig 4 - Exp 2, different configs for MIMIC data (refer sensitivity score for data processing)

  • ED Fig 5a,b, ED Fig 6a,b, Fig 8b - Exp 5, using different stratified data

  • ED Fig 8c - Exp 5 (for saving predicted probs and actual label)

SI

  • SI Fig 1 - in visualization

  • SI 2.2 different types of notes - Exp 2, different data configs

  • Table 2. Exp 3, different data

Experiments

Exp 1. Pretrain NYUTron, NYUTron-Manhattan, NYUTron-Brooklyn

Exp 2. Finetune NYUTron / other LMs for readmission prediction, mortality prediction, insurnace denial prediction, LOS prediction and comorbidity preidtion

Note:

  • insurance denial dataset (which has 2 types of notes) requires different preprocessing (see examples/supplementary/insurance_denial).
  • unlike the other downstream tasks, comorbidity prediction has 4 labels (rather than 2 labels). The current python script is compatible with both modes. To see a toy example for multi-class classification, use the commented option for data_opt in 3_make_finetune_dataset.py and use the commented hydra config (above def finetune) in 4_finetune.py.

Exp 3. Baselines: Structured Data + XGB and TF-IDF + XGB

cd baselines

source 2_run_struct_baseline.sh (configs at baselines/configs)

source 3_run_text_baseline.sh

Note:

  • struct_baseline requires GPU for categorical features.
  • Configs for structured baseline can be editted in the yaml file. Configs for text baseline can be editted in the scripts.

Exp 4. Generate redcap questions

We provided some key functions in the script baselines/4_redcap_processing.py.

generate_questions takes in two pandas dataframe (with positivly and negative labelled discharge notes) and generate redcap questions for doctors.

update_redcap_survey takes in a csv downloaded from redcap (we got this csv from our research coordinator, who kindly helped us manually configure all the questions and response fields using redcap), and added a section header and an additonal multiple choice option.

Exp 5. Evaluate auc (including stratified eval)

cd examples

python 5_eval_ckpt.py

For plotting calibration, see 8cd_calibration_curve.py

For stratified eval, need to load data for different strats (e.g., department, age, race) and potentially additional metric (e.g., roc_auc).

Exp 6. NYUTriton

We used a custom triton inference engine for prospective test. For details, see accompnaying repo NYUTriton.

Exp 7. i2b2 finetuning

cd examples/utils

python tokenize_i2b2.py

cd ..

source 6_i2b2.sh

More details about preprocessing can be found at i2b2_2012_preprocessing.