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AI‑monitored‑ICU‑Illness‑severity‑prediction‑for‑ICU

Predicting a patient's SOFA score for the next 24 or 48 hours

MSNETs -> RNN / GRU / LSTM

structure

Set-up

Operation System:

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  • MIMIC-III , which is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1].

Language and Additional Packages:

Python PyTorch Scikit-learn NumPy tqdm pandas cudatoolkit datasets matplotlib

GPU:

Nvidia

Environment

username@localhost:~$ conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
username@localhost:~$ pip install -U scikit-learn
username@localhost:~$ pip install numpy
username@localhost:~$ pip install pandas
username@localhost:~$ pip install tqdm

Quick Start

username@localhost:~$ python /src/run_training.py

Reference

[1] Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database (version 1.4). PhysioNet. https://doi.org/10.13026/C2XW26.

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Predicting a patient's SOFA score for the next 24 or 48 hours

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