Abstract: Methods and systems for displaying a healthcare condition. The methods include receiving at an interface a plurality of inputs of healthcare data associated with a patient, mapping the plurality of inputs of healthcare data to a phenotype using a first ontology system, determining at least one body portion associated with the phenotype using a second ontology system representative of body portions, and generating a first interactive display of the at least one body portion associated with the phenotype.

Abstract: Methods and systems for explaining an output from a machine learning model. The methods include receiving from a machine learning model a probability score, a plurality of features, and a contribution value for each feature; transforming the plurality of features to a reduced number of features; generating at least one cluster of a portion of a population; grouping the patient in the generated cluster; and visually presenting to a user a comparison of the reduced number of features of the profile with the plurality of features associated with the portion of the population to explain the probability score.

Abstract: Systems, methods, and apparatuses are described herein that allow users to create and manage flexible, highly modular data processing pipelines. Such pipelines may be associated with any number of connected nodes connected via dependency injection to define the location and type of data that a pipeline uses as input or output and the operations to be performed by the pipeline. The pipelines may also be associated with context information, which specifies dataset-specific configurations and includes logic required to generate and execute the associated nodes. The context information may further include logic that allows for node substitution, caching of node output, data filtering, and/or dynamic node modification.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Systems, methods and apparatuses are described herein that employ machine learning techniques to assess a likelihood or risk that one or more patients will experience an adverse outcome, such as a decline in renal function, within one or more timeframes. The embodiments may utilize patient data relating to demographics, vital signs, diagnoses, procedures, diagnostic tests, biomarker assays, genetic tests, behaviors, and/or patient symptoms, to determine risk information, such as important predictive features and patient risk scores. And the embodiments may automatically execute patient workflows, such as providing treatment recommendations to providers and/or patients, based on determined risk scores.

Abstract: Systems, methods, and apparatuses are described herein that allow users to create and manage flexible, highly modular data processing pipelines. Such pipelines may be associated with any number of connected nodes connected via dependency injection to define the location and type of data that a pipeline uses as input or output and the operations to be performed by the pipeline. The pipelines may also be associated with context information, which specifies dataset-specific configurations and includes logic required to generate and execute the associated nodes. The context information may further include logic that allows for node substitution, caching of node output, data filtering, and/or dynamic node modification.