AFFIRM

Adaptable Forecasting Framework in RealtiMe (AFFIRM) is a framework that allows the user to easily create a machine learning model that can predict adverse events in the ICU before hours before they occur. Predicting adverse events before they occur can help clinicians make decisions on how to treat the patient. The framework is specialised for predicting atrial fibrillation however it can be used to predict any adverse event.

Why use AFFIRM

It is useful as a baseline prediction for event prediction in the ICU research. It is quick and easy to use. Easily adapatable

Data

AFFIRM uses the HiRID dataset that was created by Hyland et al. https://physionet.org/content/hirid/ To use AFFIRM you will first need to get permission to use HiRID.

Getting started

Download the raw_stage data from HiRID after getting permission and place it in data_path. Also place the files from extra_files from this repository into the raw_stage folder.

./data_path/
├── raw_stage          
    ├── observation_tables       
        ├── part-0.parquet         
        ├── ...         
        ├── part-250.parquet         
    ├── pharma_records         
        ├── part-0.parquet     
        ├── ...         
        ├── part-250.parquet        
    ├── APACHE_groups.csv
    ├── drug_classes.csv 
    ├── general_table.csv
    ├── hirid_variable_reference.csv

Index

• AFFIRM * Preprocess * Prepare * Predict

Preprocess

preprocess_params = {
		      'rename_dict' : {'temp':'Temperature','mean.arterial.pressure':'MAP','systolic.arterial.pressure':'Systolic BP',
			               'diastolic.arterial.pressure':'Diastolic BP'},
		      'parameter_dict' : {'Circadian_rhythm': [4, 10]},
		      'filter_range': [0.01, 0.99]
		    }
affirm.fit_preprocess(**preprocess_params)
affirm.preprocess()

Prepare

prepare_params = {
    'predict_hours': 6,                 
    'grouping_hours': 1,
    'group_how_list': ['max'],#,'min'],
    'group_label_within':120, 
    'rolling': False,
    'take_first': False,
    'percentage_patients_per_variable': 0.8, 
    'avg_values_each': 2,
    'feature_names': [],
    'pharma_quantile' : 0.75,
    'include_patients':[],
    'exclude_patients': [],#'Surgical Cardiovascular'
    
}
affirm.fit_prepare(**prepare_params)
# affirm.prepare()

Predict

predict_params = {
    'models': {
    		'Logistic Regression': LogisticRegression(random_state=0),
		'Random Forest':RandomForestClassifier(max_depth=4, random_state=0),
		'LightGBM':lgb.LGBMClassifier(boosting_type='gbdt', objective='binary'),        
		'XGBoost': xgb.XGBClassifier(objective = "binary:logistic", eval_metric = "aucpr",use_label_encoder=False)
		},
    'colors' : {
		'LightGBM': '#4e8542',# dark green
		'Baseline': '#ff9292', #pink
		'Logistic Regression':'#eccd1c', #gold          
		'Random Forest': '#6aa4c8', #sky blye
		'XGBoost': '#ff833c', #organ
		'Optimised XGBoost': '#fcaf83',
		'Keras': '#8dd8d3' #light blue
     		},
    'n_splits': 2,
    'keep_top_features': 20,
    'intervention_features': ['Potassium', 'Magnesium']
     
}
affirm.fit_predict(**predict_params)