Machine learning methods and systems for acute respiratory distress syndrome patient sub-phenotyping

A method for determining an optimal acute respiratory distress syndrome (ARDS) classifier for ARDS sub-phenotyping, comprising:

• obtaining training data comprising ARDS patient records, wherein each ARDS patient record comprises a plurality of clinical variables and a plurality of clinical outcomes;
• determining a number of ARDS sub-phenotypes based on the ARDS patient records using latent profile analysis;
• executing an iterative process to determine an optimal ARDS classifier for determining the number of ARDS sub-phenotypes, wherein the iterative process comprises:
• constructing a plurality of machine learning classifiers, wherein,
• the constructing comprises:
• configuring an output layer of each of a plurality of machine learning classifiers based on the number of ARDS sub-phenotypes;
• configuring an input layer of each of the plurality of machine learning classifiers to accept the plurality of clinical variables;
• and
• each of the machine learning classifiers independently comprises a plurality of hyperparameters and a plurality of model parameters;
• for each of the machine learning classifiers, training and validating the machine learning classifier using a grid search with multiple-fold cross-validation on the training data to determine an optimal mode of the machine learning classifier, wherein the plurality of clinical variables of the training data are used as training input, and the clinical outcomes of the training data are used as training labels;
• and
• determining performance metrics for the optimal mode of each machine learning classifier during the multiple-fold cross-validation;
• and
• identifying the optimal ARDS classifier based on the optimal modes for each machine learning classifier of the plurality of machine learning classifiers by comparing the performance metrics.