Method for predicting risk of unfavorable outcomes, e.g., in COVID-19 hospitalization, from clinical characteristics and basic laboratory findings

A method for predicting an unfavorable outcome for a hospitalized patient, comprising the steps of:

• a) obtaining attributes from a health record for the patient comprising at least findings obtained at admission, basic patient characteristics and laboratory data,
• b) iteratively training each of a set of initial binary classifiers from a development set and child classifiers from development subsets comprising data obtained from records for a set of hospitalized patients, including at least findings obtained at admission, basic patient characteristics, and laboratory data for each of such patient;
• c) supplying the attributes obtained in step a) to a classifier using the trained set of initial binary and child classifiers implemented in a programmed computer trained to predict a risk of the unfavorable outcome, wherein:
• 1) the classifier is arranged as a hierarchical combination of (a) the initial binary classifier stratifying the patient into either a high risk group or a low risk group, and (b) the child classifiers further classifying the patient in a lowest risk group or a highest risk group depending how the initial binary classifier stratified the patient as either a member of the high risk or low risk group,
• 2) the initial binary classifier is configured as a combination of a trained classification decision tree and a logistical combination of atomic classifiers with drop-out regularization;
• and
• 3) the child classifiers are configured as a logistical combination of atomic classifiers with drop-out regularization;
• and
• generating an output specifying a predicted risk of the unfavorable outcome for the patient based on a risk group in which the patient is classified.