Pulselight Holdings, Inc.

Mallika Thanky

Jonathan William Mugan

A method for generating synthetic training records for use in training a model to predict low-incidence events. A synthetic training record is generated from a minority-class training record by substituting a different value for a feature in the minority-class training record, where the probability of the different value occurring in the minority-class training record exceeds a probability threshold. Also disclosed are a non-transitory storage medium comprising minority-class training records and synthetic training records and a method of training a machine-leaning model using training records augmented with synthetic training records. An exemplary synthetic training records is a synthetic medical record for use in training a model to predict drug overdoses.

Feature prediction for minority class data augmentation

A system of machine learning ('ML') models for making actionable predictions regarding low-incidence events, including a generative ML model that produces synthetic minority-class records to form an augmented training data set, a predictive ML model that has been trained on the augmented training data set, a certainty ML model that produces a certainty estimate, and an explanatory model that produces an explanation. A method for producing actionable predictions of a low-incidence event by applying ML models to imbalanced class data by producing a prediction by a predictive ML model that has been trained on a data set comprising synthetic minority-class data records produced by a generative ML model, and producing a certainty estimate and an explanation. At least one of the certainty estimate or explanation determines an effective or appropriate response to the prediction. The low-incidence event may comprise risk of opioid use disorder.

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Chicago, IL, United States of America

Mallika Thanky