Machine learning frameworks utilizing inferred lifecycles for predictive events
AssigneeOptum Technology, Inc.
InventorsRama Krishna Singh, Priyank Jain, Ravi Pande
Life of this patent
Estimating the term…
Abstract
There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.
Claims20
A computer-implemented method for mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events, the computer-implemented method comprising:
- processing, by one or more processors, one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event;
- determining, by the one or more processors, a filtered subset of the plurality of candidate secondary events, wherein each candidate secondary event in the filtered subset is associated with a candidate secondary event timestamp that falls within the inferred lifecycle;
- processing, by the one or more processors, the primary event code using a code co-occurrence inference machine learning model to detect a most co-occurring subset of a plurality of secondary event codes for the primary event;
- determining, by the one or more processors, the related subset based at least in part on each candidate secondary event of the plurality of candidate secondary events that is in the filtered subset and is associated with a candidate secondary event code that falls within the most co-occurring subset;
- and
- performing, by the one or more processors, one or more prediction-based actions based at least in part on the related subset.
An apparatus for mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the processor, cause the apparatus to at least:
- process one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event;
- determine a filtered subset of the plurality of candidate secondary events, wherein each candidate secondary event in the filtered subset is associated with a candidate secondary event timestamp that falls within the inferred lifecycle;
- process the primary event code using a code co-occurrence inference machine learning model to detect a most co-occurring subset of a plurality of secondary event codes for the primary event;
- determine the related subset based at least in part on each candidate secondary event of the plurality of candidate secondary events that is in the filtered subset and is associated with a candidate secondary event code that falls within the most co-occurring subset;
- and
- perform one or more prediction-based actions based at least in part on the related subset.
A computer program product for mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
- process one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event;
- determine a filtered subset of the plurality of candidate secondary events, wherein each candidate secondary event in the filtered subset is associated with a candidate secondary event timestamp that falls within the inferred lifecycle;
- process the primary event code using a code co-occurrence inference machine learning model to detect a most co-occurring subset of a plurality of secondary event codes for the primary event;
- determine the related subset based at least in part on each candidate secondary event of the plurality of candidate secondary events that is in the filtered subset and is associated with a candidate secondary event code that falls within the most co-occurring subset;
- and
- perform one or more prediction-based actions based at least in part on the related subset.
Dependencies are inferred from the claim text.