# ClinicalNotesICU

**Repository Path**: lyyl9001/ClinicalNotesICU

## Basic Information

- **Project Name**: ClinicalNotesICU
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-02-08
- **Last Updated**: 2024-02-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

This is code for the paper, "Using Clinical Notes with Time Series Data for ICU Management" at EMNLP 2019 by Swaraj Khadanga, Karan Aggarwal, Shafiq R. Joty, Jaideep Srivastava. 

# Steps
Clone https://github.com/YerevaNN/mimic3-benchmarks and run all data generation steps to generate training data without text features.

## Text Scripts
1. Run extract_notes.py file under scripts folder.
2. Run extract_T0.py file under scripts folder.

## Configuration
1. Update all paths and configuration in config.py file.

## Models.
1. For IHM run ihm_model.py file under tf_trad.

    Number of train_raw_names:  14681 <br>
    Succeed Merging:  11579 - Model will train on this many episodes as it contains text. <br>
    Missing Merging:  3102 - These texts don't have any text for first 48 hours. 

2. For Decompensation, run decom_los_model.py file under tf_trad.

    Text Not found for patients:  6897 <br>
    Successful for patients:  22353

3. Lenght of Stay, run decom_los_model.py file under tf_trad.

    Successful for episodes for training:  22353
    
 
 ## Credits
 The code is based on one of the popular MIMIC-3 benchmark repository https://github.com/YerevaNN/mimic3-benchmarks for the experiemntal setup and evaluation metrics.