Systems and methods for predictive analyses with machine learning systems

A system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a dataset including transition data and factor data, the transition data being associated with progression of a condition from a first stage to a second stage, the factor data being associated with one or more factors that influence progression of the condition from the first stage to the second stage;
• generating, by a data analysis module, a feature for a machine learning model based on the transition data, the data analysis module comprising:
• a feature extractor that identifies the transition data and the factor data based on analyses of structured text documents in the dataset and unstructured documents in the dataset to generate the feature;
• a structured document analyzer that processes the structured text documents from the dataset for input into the feature extractor;
• and
• an unstructured document analyzer that prepares the unstructured documents from the dataset for processing by a natural language processor for input into the feature extractor;
• generating, via input of at least the feature into the machine learning model, one or more data objects indicative of a transition prediction for a transition from the first stage to the second stage, the machine learning model having been trained with:
• (i) data sources including training factor data having information other than a chemical constituent of blood, (ii) previously-generated features extracted by the feature extractor, and (iii) previously-generated transition predictions for the transition from the first stage to the second stage, to output information associated with a transition prediction;
• and
• initiating performance of one or more remedial or analytical actions in response to generating the one or more data objects indicative of the transition prediction.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a dataset including transition data and factor data, the transition data being associated with progression of a condition from a first stage to a second stage, the factor data being associated with one or more factors that influence progression of the condition from the first stage to the second stage;
• generating, by a data analysis module, a feature for a machine learning model based on the transition data, the data analysis module comprising:
• a feature extractor that identifies the transition data and the factor data based on analyses of structured text documents in the dataset and unstructured documents in the dataset to generate the feature;
• a structured document analyzer that processes the structured text documents from the dataset for input into the feature extractor;
• and
• an unstructured document analyzer that prepares the unstructured documents from the dataset for processing by a natural language processor for input into the feature extractor;
• generating, via input of at least the feature into the machine learning model, one or more data objects indicative of a transition prediction for a transition from the first stage to the second stage, the machine learning model having been trained with:
• (i) data sources including training factor data having information other than a chemical constituent of blood, (ii) previously-generated features extracted by the feature extractor, and (iii) previously-generated transition predictions for the transition from the first stage to the second stage, to output information associated with a transition prediction;
• and
• initiating performance of one or more remedial or analytical actions in response to generating the one or more data objects indicative of the transition prediction.