Abstract: A method of optimizing online advertising campaign allocations is disclosed. It is determined that an auction for a set of advertising slots has been triggered. It is identified that the advertising campaigns are configured to bid on the set of advertising slots. A ranking score for each of the advertising campaigns is determined. The ranking scores are adjusted for each cost-per-click advertising campaign of the set of advertising campaigns by an adjustment factor specific to a context of the auction. The set of advertising slots is allocated to the winners of the auction. The winners of the auction are communicated for integration into a content page.

Abstract: A method and apparatus for populating content items into a feed is provided. The feed comprises a sequence of content item ordered in such a way as to maximize a number of content items displayed to a user by virtue of the user scrolling down through the feed. The content items are each associated with a click-through rate, an indication of a number of times the content has been displayed to users, an indication of a number of times that the users have scrolled to a next item in the feed after the item was displayed, and a height of the content item. These values are used to train a behavioral model and then used by the behavioral model to layout the content items in a feed rendered at a user device.

Abstract: A method and apparatus for populating content items into a feed is provided. The feed comprises a sequence of content item ordered in such a way as to maximize a number of content items displayed to a user by virtue of the user scrolling down through the feed. The content items are each associated with a click-through rate, an indication of a number of times the content has been displayed to users, an indication of a number of times that the users have scrolled to a next item in the feed after the item was displayed, and a height of the content item. These values are used to train a behavioral model and then used by the behavioral model to layout the content items in a feed rendered at a user device.

Abstract: A method of optimizing online advertising campaign allocations is disclosed. It is determined that an auction for a set of advertising slots has been triggered. It is identified that the advertising campaigns are configured to bid on the set of advertising slots. A ranking score for each of the advertising campaigns is determined. The ranking scores are adjusted for each cost-per-click advertising campaign of the set of advertising campaigns by an adjustment factor specific to a context of the auction. The set of advertising slots is allocated to the winners of the auction. The winners of the auction are communicated for integration into a content page.

Abstract: A system and method are provided for automatically selecting one of multiple formats in which to serve a content item. The system collects data regarding content items served and user activity and/or revenue regarding those served items. These data are used to calculate performance values or scores of each format for specified factors such as destination (e.g., a web domain, a URL, a content channel), visibility (e.g., above the fold), a period of time, a vertical or type of content, and so on. When a new content request is received, the format selected for serving in response to the request is chosen based on the competing formats' calculated performances, and a suitable content item is selected. The selected format may be the format likely to generate the most revenue, may be selected by statistical sampling, or may be selected by using the performance values/scores in some other way.

Abstract: A system and method are provided for automatically selecting one of multiple formats in which to serve a content item. The system collects data regarding content items served and user activity and/or revenue regarding those served items. These data are used to calculate performance values or scores of each format for specified factors such as destination (e.g., a web domain, a URL, a content channel), visibility (e.g., above the fold), a period of time, a vertical or type of content, and so on. When a new content request is received, the format selected for serving in response to the request is chosen based on the competing formats' calculated performances, and a suitable content item is selected. The selected format may be the format likely to generate the most revenue, may be selected by statistical sampling, or may be selected by using the performance values/scores in some other way.

Abstract: A medical concept is learned about or inferred from a medical transcript. A probabilistic model is trained from medical transcripts. For example, the problem is treated as a graphical model. Discrimitive or generative learning is used to train the probabilistic model. A mutual information criterion can be employed to identify a discrete set of words or phrases to be used in the probabilistic model The model is based on the types of medical transcripts, focusing on this source of data to output the most probable state of a patient in the medical field or domain.

Abstract: Medical related quality of care information is extracted and edited for reporting. Patient records are mined. The mining may include mining unstructured data to create structured information. Measures are derived automatically from the structured information. A user may then edit the measures, data points used to derive the measures, or other quality metric based on expert review. The editing may allow for a better quality report. Tools may be provided to configure reports, allowing generation of new or different reports.

Abstract: Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.

Abstract: A method for finding a ranking function ƒ that classifies feature points in an n-dimensional space includes providing a plurality of feature points xk derived from tissue sample regions in a digital medical image, providing training data A comprising training samples Aj where A = ? j = 1 S ? ( A j = { x i j } i = 1 m j ) , providing an ordering E={(P,Q)|APAQ} of at least some training data sets where all training samples xi?AP are ranked higher than any sample xj?AQ, solving a mathematical optimization program to determine the ranking function ƒ that classifies said feature points x into sets A. For any two sets Ai, Aj, AiAj, and the ranking function ƒ satisfies inequality constraints ƒ(xi)?ƒ(xj) for all xi?conv(Ai) and xj?conv(Aj), where conv(A) represents the convex hull of the elements of set A.

Abstract: A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.