摘要:
Systems and methods are provided for predicting rare events, such as hospitalization events. A set of data records, each containing multiple attributes with one or more values (which may include an “unknown” value), may represent a root node of a decision tree. This root node may be partitioned based on one of the attributes, such that the concentration (e.g., “purity”) of a relevant outcome (e.g., the rare event) is increased in one node and decreased in another. This process may be repeated until a decision tree with sufficiently pure leaf nodes is created. This “purified” decision tree may then be used to predict one or more rare events.
摘要:
Systems and methods are provided that allow a user to monitor, diagnose, and configure a process. A user interface may be presented that displays data received from process monitors, sensors, and multivariate models. The user interface may be interactive, allowing a user to select which composite and multivariate models are displayed. The user interface and multivariate model may be constructed and updated in real time. The user interface may present various data and interfaces, such as a representation of a composite variable in a multivariate model, a representation of the contribution of process variables to the composite variable, and a representation of a subset of the process variables. The user may select a point of the composite variable for analysis, and the interface may indicate the contribution and values of process variables at the selected point. The interface may be transmitted to a remote user.
摘要:
Systems and methods are provided for predicting rare events, such as hospitalization events. Data related to health and/or healthcare may be compiled from a number of sources and used to construct a predictive model. The predictive model employ Principal Component Analysis (PCA) and Partial Least Squares (PLS). The data may be arranged in a timeline, and formatted in such a way as to provide discrete temporal “batches”. This arrangement may facilitate the PCA and PLS decomposition of the data into predictive models. These models may then be applied to an individual's data, to create a prediction of healthcare related events.
摘要:
Systems and methods are provided that allow a user to monitor, diagnose, and configure a process. A user interface may be presented that displays data received from process monitors, sensors, and multivariate models. The user interface may be interactive, allowing a user to select which composite and multivariate models are displayed. The user interface and multivariate model may be constructed and updated in real time. The user interface may present various data and interfaces, such as a representation of a composite variable in a multivariate model, a representation of the contribution of process variables to the composite variable, and a representation of a subset of the process variables. The user may select a point of the composite variable for analysis, and the interface may indicate the contribution and values of process variables at the selected point. The interface may be transmitted to a remote user.