Abstract: A system of machine learning (“ML”) models for making actionable predictions regarding a low-incidence event, including a predictive ML model that has been trained on an augmented training data set comprising synthetic minority-class records to produce a prediction and a certainty ML model that produces a certainty estimate. A method of applying ML models to make an actionable prediction, including training a predictive ML model to make a prediction regarding a low-incidence event from a medical record, applying the predictive ML model to medical records to produce a prediction, and applying a certainty model to generate a certainty estimate. The low-incidence event may comprise risk of opioid use disorder.

Abstract: A computer-implemented method of protecting confidentiality when generating synthetic training records for machine learning from sensitive data records, comprising a source computer system connected to a separate target computer system, where the target computer system comprises sensitive data records comprising private or confidential data. The source computer system performs the functions of a generator component of a generative adversarial network (GAN) and the target computer system performs the functions of a discriminator component of the GAN, where the generator and discriminator functions of the GAN are distributed between the source and target computer systems. Synthetic training records are generated using a computational process that does not reveal contents of the sensitive data records. Also disclosed is a method of training a machine-learning model using the one or more synthetic training records.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A a computer-implemented method of creating synthetic training data records for machine learning, comprising a source computer system operatively connected to a separate target computer system, where the target system comprises real data records, the contents of which are not known to the source computer system. The source computer system comprises the generator component of a generative adversarial network (GAN) and the target computer system comprises a discriminator component of the GAN. The source computer system generates one or more synthetic training records using a computational process that does not reveal contents of the real data records. Also disclosed is a method of training a machine-learning model using the one or more synthetic training records.

Abstract: A computer-implemented method of creating synthetic training data records for machine learning, comprising a source computer system operatively connected to a separate target computer system, where the target system comprises real data records, the contents of which are not known to the source computer system. The source computer system comprises the generator component of a generative adversarial network (GAN) and the target computer system comprises a discriminator component of the GAN. The source computer system generates one or more synthetic training records using a computational process that does not reveal contents of the real data records. Also disclosed is a method of training a machine-learning model using the one or more synthetic training records.

Abstract: The present invention is a method and apparatus, including computer programs encoded on computer storage media, for checking grammar in text. An edit generator and edit scorer are provided. The edit generator creates edited versions of the text that are scored by the edit scorer. The edit scorer provides an encoder and a decoder. The encoder converts the text into an abstract representation that is used by the decoder to score edited versions of the text. The invention can also be used as a thesaurus and idiom finder, generating alternatives to words and phrases, and scoring their viability. The invention can also correct text in queries for items.

Abstract: The present invention is a system and method for uploading an owner's personality to a digital environment and enabling a cyborg to work on the owner's behalf in that environment. The cyborg may take the actions that the owner would take in that digital environment. The cyborg may also act as a filter and use the personality of the owner to determine which events in the digital environment the owner would like to see. This invention allows an owner to increase his or her reach in the digital environment by automating tasks.