Abstract: A system and computer-implemented method for identifying fraudulent healthcare providers receives raw claims data from one or more data sources. The raw claims data includes claims associated with a selected healthcare provider. Each of the claims includes one or more claim lines. A first model is executed on the raw claims data. The first model determines a first score for the healthcare provider. A second model is executed on the raw claims data. The second model determines a second score for the healthcare provider. In addition, a third model is executed on the raw claims data. The third model determines a third score for the healthcare provider. A final provider-level risk score is determined for the healthcare provider based on the first, second, and third scores.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.

Abstract: Exemplary systems for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary system includes a computing device configured to access phenotypic data related to a pool of hybrids from a data structure and determine a prediction score for each of the hybrids in the pool of hybrids based on the accessed phenotypic data. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The computing device is also configured to select a group of hybrids from the pool of hybrids based on the prediction score, identify a set of hybrids, from the selected group of hybrids, based on one or more factors associated with the hybrids, and then direct the set of hybrids to a validation phase of the plant breeding pipeline for planting and/or testing.

Abstract: Exemplary methods for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of hybrids and determining a prediction score for at least a portion of the hybrids included in the pool based on the data included in the data structure. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The method further includes selecting a group of hybrids from the pool based on the prediction score, identifying a set of hybrids, from the group of hybrids, based on an expected performance of the set of hybrids and/or one or more factors associated with the hybrids and/or lines making up the hybrids, and also directing the set of hybrids to a further iteration or different phase in the breeding pipeline.

Abstract: Exemplary methods for identifying hybrids for use in a plant breeding pipeline are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of hybrids and determining a prediction score for at least a portion of the hybrids included in the pool based on the data included in the data structure. The prediction score is indicative of a probability of selection and/or a probability of success of the hybrid based on historical data. The method further includes selecting a group of hybrids from the pool based on the prediction score, identifying a set of hybrids, from the group of hybrids, based on an expected performance of the set of hybrids and/or one or more factors associated with the hybrids and/or lines making up the hybrids, and also directing the set of hybrids to a further iteration or different phase in the breeding pipeline.

Abstract: Exemplary methods for identifying progenies for use in plant breeding are disclosed. One exemplary computer-implemented method includes accessing a data structure including data representative of a pool of progenies and determining a prediction score for at least a portion of the pool of progenies based on the data included in the data structure. The prediction score indicates a probability of selection of the progeny based on historical data. The method further includes selecting a group of progenies from the pool of progenies based on the prediction score, identifying a set of progenies, from the group of progenies, based on at least one of an expected performance of the group of progenies and at least one factor associated with the set of progenies, the pool of progenies and/or the group of progenies, and directing the set of progenies into a validation phase of a breeding pipeline.