Abstract: Various information systems may benefit from the appropriate simplification of data structures and processes. For example, certain health information systems may benefit from dimension reduction of claims data. A method can include creating a tensor to organize data. The method can also include conditionally reducing the size of that tensor in order to more effectively apply machine learning.

Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: Various information systems may benefit from the appropriate simplification of data structures and processes. For example, certain health information systems may benefit from dimension reduction of claims data. A method can include creating a tensor to organize data. The method can also include conditionally reducing the size of that tensor in order to more effectively apply machine learning.

Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: Certain embodiments of the present invention relate generally to using machine learning or other automated techniques to among other things, identify, estimate, and/or predict patient health conditions. Furthermore, certain embodiments of the present invention are related to health interventions performed to reduce the risk of developing diseases and health conditions. This risk reduction improves the overall health of the individual and/or the population and helps reduce healthcare costs.

Abstract: Various areas of medicine and healthcare may benefit from improvements in the identification of and mitigation of health risks. For example, medicine and healthcare may benefit from systems and methods that can mine literature to select and analyze the risk factor(s) contributing to the development, progression and management of common health conditions. A method can include receiving an input health condition. The method can also include scoring the health condition based on a plurality of risk factor sources to generate a health score. The method can further include providing the health score and at least one remediation goal based on the risk factor sources.

Abstract: Risk engines and related methods may benefit patients and their physicians. For example, patients may benefit from being able to determine their personal genetic, environmental, and behavioral risks. Moreover, physicians may be able to provide statistically-driven individual recommendations based on a risk engine's determination of such risks. A method can include selecting one or more candidate genetic variants associated with a phenotype from the scientific literature. The method can also include scoring a genetic association between the one or more candidate genetic variants and the phenotype. The method can further include selecting one or more high-scoring genetic variants. Selecting a best genetic variant within each of at least one linkage disequilibrium (LD) block can also be included in the method. The method can additionally include calculating risk associated with the best genetic variant from the at least one LD block.