This repository is initially developed for a group project from a Gatech OMSCS class. The version delivered during the class is tagged as v0.1.

The implementation pipeline is summarized in the following figure.

In order to access MIMIC-III database on Google Cloud, please follow the instructions in https://physionet.org/content/mimiciii/1.4/.

Run sql and python code in following orders to recreate the csv files used in modeling.

Cohort.sql
hourly_cohort.sql
static-query.sql
match-control.py
extract-48h-of-hourly-case-lab-series.sql
extract-48h-of-hourly-case-vital-series.sql
extract-48h-of-hourly-control-lab-series.sql
extract-48h-of-hourly-control-vital-series.sql
data_prep_step1.py

All sql codes can be run on GCP BigQuery console by changing project and data names to your own location accordingly. Alternatively, one can run the first three queries with get_cohort.sh on cloud shell with project id as the lone argument. Similarly, the other four sequence queries can be run with extract_sequence.sh.

The two python files can be run as follows assuming you are in the top level folder

python Python/match-control.py -c <bigquery credential json filename> -t <bigquery table reference>
python Python/data_prep_step1.py -c <bigquery credential json filename> -t <bigquery table reference> -w <prediction window hours(default is 3)>

and an example would be

python Python/match-control.py -c bdfh.json -t cdcproject.BDFH
python Python/data_prep_step1.py -c bdfh.json -t cdcproject.BDFH -w 3

The data will be saved in the Data folder.

Note: while we tried to set the random seed whenever possible for reproducibility, there still might be some factors we overlooked which might cause differences in model input data.

Final model data processing is done in Python/model_data.py.

• Logistic regression: trained and evaluated in LR.ipynb
• SVM: trained and evaluated in SVM_Model.ipynb
• LightGBM: trained and evaluated in Lightgbm_Model.ipynb
• RNN: trained using python Python/rnn_main.py, evaluated in RNN_evaluation.ipynb

• Data pipeline: Detailed data filtering and label definition: Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping, paper, code
• Original sql codes based on MIMIC-III can be found in https://github.com/MIT-LCP/mimic-code
• Reference library: https://www.zotero.org/groups/4456592/bdfh_project/items/7JQNQF39/library