Abstract: Disclosed is a system and method for providing personalized health information. The disclosed systems and methods provide for analyzing self-reported data relating to personal health information in order to predict a health condition. The systems and methods disclosed provide personalized interpretations of medical knowledge in light of the growing collection of personal health information that is publicly and privately available. Accordingly, the present disclosure provides systems and methods for personalized information intermediation to help individuals to navigate the growing selection of personal health products and services, and to contribute to health care system efficiencies by improving individual health knowledge. In some embodiments, the systems and methods disclosed provide fertility prediction encompassing a predicted date of ovulation and fertility window.

Abstract: Disclosed is a system and method for providing personalized health information. The disclosed systems and methods provide for analyzing self-reported data relating to personal health information in order to predict a health condition. The systems and methods disclosed provide personalized interpretations of medical knowledge in light of the growing collection of personal health information that is publicly and privately available. Accordingly, the present disclosure provides systems and methods for personalized information intermediation to help individuals to navigate the growing selection of personal health products and services, and to contribute to health care system efficiencies by improving individual health knowledge. In some embodiments, the systems and methods disclosed provide fertility prediction encompassing a predicted date of ovulation and fertility window.

Abstract: The invention relates to cross-document entity co-reference systems in which naturally occurring entity mentions in a document corpus are analyzed and transformed into name clusters that represent global entities. In a first aspect of the invention, a name variation module analyzes naturally occurring names of entities extracted from the document corpus and provides an initial set of equivalent names that could refer to the same real world entity. In a second aspect of the invention, a disambiguation module takes the initial set of equivalent names and uses an agglomerative clustering algorithm to disambiguate the potentially co-referent named entities.

Abstract: The invention relates to cross-document entity co-reference systems in which naturally occurring entity mentions in a document corpus are analyzed and transformed into name clusters that represent global entities. In a first aspect of the invention, a name variation module analyzes naturally occurring names of entities extracted from the document corpus and provides an initial set of equivalent names that could refer to the same real world entity. In a second aspect of the invention, a disambiguation module takes the initial set of equivalent names and uses an agglomerative clustering algorithm to disambiguate the potentially co-referent named entities.