Authors: Zack Goldblum, Josh Miller, Kevin Ramirez Chavez
Final project for BMES 550 Advanced Computational Languages at Drexel University
This project provides a GUI for clinicians to predict the outcome of a neurocritical care patient. The parameters input for a current patient are compared to a database of retrospectively collected patients and medical data using a k-nearest neighbors algorithm. The utilized database is the Medical Information Mart for Intensive Care (MIMIC)-IV.
Reference: Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2022). MIMIC-IV (version 2.1). PhysioNet. https://doi.org/10.13026/rrgf-xw32.
Install the necessary packages:
pip install wxPython
pip install ipykernel
pip install notebook
pip install pandas
pip install numpy
This project was created using Python version 3.10.8.
1 - Download all files into a directory on your computer.
2 - Open create_mimic_iv_sql.ipynb.
3 - Execute the first code cell in the notebook.
4 - Extract the following MIMIC-IV datasets to C:/Users/USERNAME/AppData/Local/Temp/bmes/final_project_csv.
hosp/admissions.csv
hosp/d_icd_diagnoses.csv
hosp/diagnoses_icd.csv
hosp/patients.csv
hosp/pharmacy.csv
icu/icustays.csv
5 - Run the rest of the notebook.
6 - Run gui.py:
python gui.py
7 - Enter the patient information and click "Submit all". The predicted patient outcome results are displayed at the bottom of the GUI.
