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dataset
notebooks
src
variables
readme.rst

readme.rst

README

This repository includes analysis scripts and modules for working with the HCUP readmission dataset.

Dataset folder

Place the NRD databases unzipped in the dataset folder.

Src folder

The src folder includes neural models implementation using PyTorch (version 0.3.x), in addition to training/evaluation workflow and utilities found in the train_eval.py and utilities.py modules respectively.

Notebooks

The notebooks folder includes jupyter notebooks to perform the following:

Data processing for the HCUP NRD databases

Neural models

  • neural_models_dataset_gen.ipynb: To prepare and generate the dataset in a suitable format for training/evaluating the neural models using 5-folds cross validation.
  • optimize_hyperparams_neural_models.ipynb: Run this notebook to identify quasi-best hyperparams for neural models.
  • train_eval_neural_models.ipynb: Train and evaluate the neural models.

Baseline models

  • train_eval_baseline_models.ipynb: Train and evaluate baseline models.

Performance evaluation

  • performance_evaluation_decoded_output.ipynb: To post-process decoded output (i.e. predicted outcomes of the models) for generating performance evaluation.
  • run_cvAUC_R.ipynb: Run the cvAUC package to analyze AUC (area under the ROC curve) performance of the models on the 5-folds (R kernel should be installed).
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