#NEC

"Interpretable Prediction of Necrotizing Enterocolitis from Machine Learning Analysis of Premature Infant Stool Microbiota", by Yun Chao Lin, Ansaf Salleb-Aouissi and Thomas A. Hooven

Setup

This code uses Python 3.7 virtual environment can be use with requirement.txt.

python3 -m venv ~/venv/nec
source ~/venv/nec/bin/activate
pip install -r requirements.txt

Running the model

The main script are src/main.py. All script codes are stored in src

Example of usage: python src/main.py -d <dataset_path> -ne <number_of_epochs> -lr <learning_rate>

The main.py runs the MIL model and produces the ROC and Precision and Recall Curves.

Karken2 and Hierarchical Feature Engineering

Data is preprocessed using Karken2 (https://ccb.jhu.edu/software/kraken2/) and HFE (https://github.com/HenschelLab/HierarchicalFeatureEngineering). Codes are available on link provided.

Dataset and Target Label

Each sample must contain a SUBJECTID. A patient can be represented by multiple samples with same SUBJECTID. Target label is whether a patient has NEC or not. A sample with same SUBJECTID has the same target label.

Model

Model are implemented in pytorch which originates from (https://github.com/AMLab-Amsterdam/AttentionDeepMIL). Codes are stored in src/model.