| 摘要 |
A method, system, and/or computer program product automatically abstracts and selects an optimal set of variance-related features that are indicative of an individual outcome and personalized plan selection in health care. An abstracted set of candidate variance-related patient features, which comprise temporally heteroskedastic features, is generated. Each patient feature from the abstracted set of candidate variance-related patient features is optimized by identifying a time period in which variances and heteroskedasticity of each patient feature are maximized, where the optimizing creates an optimal abstracted set of variance-related patient features from the time period in which the variances and heteroskedasticity of each patient feature are maximized. The optimal abstracted set of variance-related patient features is then used for a current patient to predict a particular outcome and/or to create a personalized health care treatment plan. |
| 主权项 |
1. A method to automatically abstract and select an optimal set of variance-related features that are indicative of an individual outcome in health care, the method comprising:
generating, by one or more processors, an abstracted set of candidate variance-related patient features, wherein the abstracted set of candidate variance-related patient features are temporally heteroskedastic features; optimizing, by one or more processors, each patient feature from the abstracted set of candidate variance-related patient features by identifying a time period in which variances and heteroskedasticity of each patient feature are maximized, wherein said optimizing creates an optimal abstracted set of variance-related patient features from the time period in which the variances and heteroskedasticity of each patient feature are maximized; comparing, by one or more processors, the optimal abstracted set of variance-related patient features to a historical set of data for a population of patients to create a predictive set of variance-related patient features, wherein the predictive set of variance-related patient features predicts a target health-related outcome of the population of patients; generating, by one or more processors, a current patient optimal set of variance-related patient features for a current patient; comparing, by one or more processors, the optimal set of variance-related patient features for the population of patients to the current patient optimal set of variance-related patient features for the current patient; in response to the optimal set of variance-related patient features for the population of patients matching the current patient optimal set of variance-related patient features for the current patient within a predefined limit, determining, by one or more processors, whether the target health-related outcome matches a predefined health-related outcome for the current patient; and in response to the target health-related outcome matching the predefined health-related outcome for the current patient, issuing, by one or more processors, an alert related to the predefined health-related outcome for the current patient. |
