Fast and Interpretable Mortality Risk Scores for Critical Care Patients

This repository contains the code implementation for experiments in the paper: Fast and Interpretable Mortality Risk Scores for Critical Care Patients. We detail the specific steps to reproduce our results below. Our work has been accepted by JAMIA.

Setup 🛠️

• Install packages using requirements.txt.
• Set PYTHONPATH to src/common. This is because most of the scripts use modules in mimic_pipeline directory.

Data 📊

Data Source

• MIMIC III: https://physionet.org/content/mimiciii/1.4/.
• eICU: https://physionet.org/content/eicu-crd/2.0/.

Data Preprocessing for MIMIC III

1. Follow the official tutorial for MIMIC III to build a local postgres database (https://mimic.mit.edu/docs/gettingstarted/local/install-mimic-locally-ubuntu/).
2. Create concepts for MIMIC III using the code in official repository (https://github.com/MIT-LCP/mimic-code/tree/main/mimic-iii/concepts_postgres).
3. Run src/common/sql/mimic3/extract/union_features.sql and src/common/sql/mimic3/extract/union_features_prep.sql. This step preprocesses the features considered in our study, selects our study cohorts, and generates a tabular data for the patients.
4. Use src/exp_6.6_to_6.27/union_train_test.ipynb to generate train and test splits for MIMIC III study cohort.

Data Preprocessing for eICU

1. Similarly as MIMIC III, build a local postgres database (https://eicu-crd.mit.edu/tutorials/install_eicu_locally/). Then, create concepts for eICU using code in officla repository (https://github.com/MIT-LCP/eicu-code/tree/main/concepts).
2. Run src/common/sql/eicu/extract/union_features.sql, this script selects the cohorts for our study and generates the data in a tabular form.
3. Use src/exp_6.6_to_6.27/union_eicu_generate.ipynb to save the data as a .csv file locally.

Results 📚

Figure 1

1. Use src/exp_6.6_to_6.27/mimic_groupfasterrisk_train.py to train GFR models. Set group sparsity as 15. This creates the models without monotonicity constraints.
2. To generate risk scores with monotonicity constraints, run src/exp_6.6_to_6.27/card_generation.ipynb.

Figure 3b

1. Use src/exp_6.6_to_6.27/mimic_cross_validation.py to perform nested cross valiadtion on MIMIC III. Please note that the hyperparameters in that file are tuned individually on each of the five folds on MIMIC III. If you would like to reproduce our results for hyperparameter optimization, perform the following steps: (1) run src/exp_6.6_to_6.27/mimic_kfold_generation.ipynb to generate 5 folds for MIMIC III; (2) use src/exp_6.6_to_6.27/sweep.py and src/exp_6.6_to_6.27/sweep_oasis.py to perform hyperparameter optimization. The possible hyperparameter combinations are stored at params directory.
2. Run src/exp_6.6_to_6.27/mimic_baselines.ipynb to obtain results for OASIS and SAPS II on MIMIC III.
3. Run src/exp_6.6_to_6.27/OOD.ipynb to obtain results for OASIS, SAPS II, and APACHE IV/IVa on eICU.

Figure 3a

1. Run src/exp_6.6_to_6.27/OOD_visualize.ipynb to visualize the ROC and PR curves on eICU.

Figure 4a

1. Use src/exp_6.6_to_6.27/mimic_cross_validation.py, train GroupFasterRisk models with group sparsity of 10, 15, 20, 25, 30, 35, 40, and 45.
2. After training is complete, run the Group Sparsity cell in src/exp_6.6_to_6.27/visualize.ipynb.

Figure 4b

1. Run src/exp_6.6_to_6.27/time_fasterrisk.py to obtain an estimate of runtime for training GroupFasterRisk models.
2. Plot the figure using Time Consumption cell in src/exp_6.6_to_6.27/visualize.ipynb.

Figure 5a

1. Obtain tabular data for each disease-specific cohorts using src/exp_6.6_to_6.27/union_disease_generate.ipynb. Then, generate the folds for MIMIC III subpopulations using src/exp_6.6_to_6.27/mimic_kfold_disease_generation.ipynb.
2. Train GroupFasterRisk models on MIMIC III subpopulation using src/exp_6.6_to_6.27/mimic_cross_validation_disease.py.
3. Plot the results using Disease Specific cell in src/exp_6.6_to_6.27/visualize.ipynb.

Figure 5b

1. Run src/exp_6.6_to_6.27/OOD_disease.ipynb to obtain the results.

Figure 6

1. Use src/exp_6.6_to_6.27/mimic_feature_selection.py to train ML models. Train one set of models with GroupFasterRisk features, and train another set of models with OASIS features.
2. Run Feature Selection cell in src/exp_6.6_to_6.27/visualize.ipynb.

Figure 7

1. Use src/exp_6.6_to_6.27/mimic_cross_validation.py to train GFR-14, GFR-OASIS, and GFR-40. These models are for internal evaluation (MIMIC III).
2. Use src/exp_6.6_to_6.27/mimic_cross_validation.py to train ML models. Train one set of models with OASIS features, and train another set of models with all 49 features. These models are for internal evaluation (MIMIC III).
3. Use src/exp_6.6_to_6.27/mimic_groupfasterrisk_train.py to train GFR-14, GFR-OASIS, and GFR-40 for out-of-distribution evaluation (eICU).
4. Use src/exp_6.6_to_6.27/mimic_ml_train.py to train ML models. Similarly, train one set of models with OASIS features, and train another set of models with all 49 features. These models are for out-of-distribution evaluation (eICU).
5. The results for AutoScore on both MIMIC III and eICU can be obtained with src/exp_6.6_to_6.27/autoscore.r.
6. Use Complexity Graph cell in src/exp_6.6_to_6.27/visualize.ipynb to generate the figures.

Table 1

1. Use src/exp_6.6_to_6.27/OOD_calibrate.ipynb to calibrate GFR models on eICU dataset using a subset of 2000 patients.
2. Use src/exp_6.6_to_6.27/OOD_fairness.ipynb to obtain the numerical results in Table 1.

Citation

@article{zhu2023fast,
  title={Fast and Interpretable Mortality Risk Scores for Critical Care Patients},
  author={Zhu, Chloe Qinyu and Tian, Muhang and Semenova, Lesia and Liu, Jiachang and Xu, Jack and Scarpa, Joseph and Rudin, Cynthia},
  journal={arXiv preprint arXiv:2311.13015},
  year={2023}
}