Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: A system and method for intermediate landing page rerouting are provided. In example embodiments, determine whether a webpage associated with a hyperlink has corresponding social network activities. Extract content from the webpage determined to have corresponding social network activities. In response to a selection of the hyperlink, reroute a web browser to an intermediate landing page. Cause presentation, at a user interface, of the extracted content and the corresponding social network activities.

Abstract: A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message, determining a threshold differential and a weight value using responses to the plurality of messages, and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages transmitted to target groups of an online social networking service, determining a threshold differential and a weight value using responses to the plurality of messages, and send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

Abstract: Techniques are provided for optimization of user interactions based on connection value scores. A method may comprise receiving input that indicates a selection of one or more social network activities to optimize for a particular member of a social network. The method may also comprise, based on the selection of the one or more social network activities, calculating a connection value score for a social network connection between the particular member and each user in a plurality of other users. Calculating the connection value score may comprise identifying one or more attribute values that are correlated with the one or more social network activities. Further, the method may comprise, based on the connection value score for each user of the plurality of other users, determining a ranking of the plurality of other users. The method may also comprise determining content based on the ranking of the plurality of other users.

Abstract: This disclosure relates to systems and methods for managing multiple messages. In one example, a method includes determining a message transmission frequency threshold for a member of an online social networking service using responses from the member; receiving a message that is to be transmitted to the member; storing the message, without transmitting the message to the member, in a digest of messages for the member; and transmitting the digest to the member in response to a send score for the digest exceeding a send score threshold, the send score calculated using the number of messages in the digest.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums for matching mentee members with mentor members. The member matching may utilize social networking service data and one or more preferences of both the potential mentees and potential mentors. For example, after indicating an interest in being mentored (e.g., being a mentee), a member may be presented with a list of potential mentors that are selected, scored, and in some examples, ranked based upon the member's preferences, the potential mentors' preferences, and other compatibility factors. The member may then select one or more of these potential mentors.

Abstract: A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.

Abstract: This disclosure relates to systems and methods for selecting an application to handle a message. In one example, a method includes receiving a first message from a first member of an online social networking service using a first computing device, the first message addressed to a second member of the online social networking service, determining which application the first member used to generate the first message, receiving a second message from the second member, the second message being a reply message to the first message, and transmitting the second message to the first computing device that includes an instruction that the determined application is to handle the second message.

Abstract: A system and method for intermediate landing page rerouting are provided. In example embodiments, determine whether a webpage associated with a hyperlink has corresponding social network activities. Extract content from the webpage determined to have corresponding social network activities. In response to a selection of the hyperlink, reroute a web browser to an intermediate landing page. Cause presentation, at a user interface, of the extracted content and the corresponding social network activities.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for one or more message and message recipient pairs in the set of input messages, setting a send constraint for one or more of the pairs using a send threshold for the message in the set and the random value, and sending the message to a recipient for the message in the set in response to the send constraint for the pair being satisfied.

Abstract: This disclosure relates to systems and methods for managing multiple messages. In one example, a method includes determining a message transmission frequency threshold for a member of an online social networking service using responses from the member; receiving a message that is to be transmitted to the member; storing the message, without transmitting the message to the member, in a digest of messages for the member; and transmitting the digest to the member response to a send score for the digest exceeding a send score threshold, the send score calculated using the number of messages in the digest.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, solving, for a set of input messages, a multi-objective optimization problem to minimize a number of messages to send while satisfying one or more constraints, selecting a random value for a message in the set, setting a send constraint for the message in the set using the send threshold for the message in the set and the random value, and sending the message in the set in response to the send constraint being satisfied.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message, determining a threshold differential and a weight value using responses to the plurality of messages, and sending the input message in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being above the threshold differential.

Abstract: This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages transmitted to target groups of an online social networking service, determining a threshold differential and a weight value using responses to the plurality of messages, and send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

Abstract: In an embodiment, a method may comprise acquiring behavioral data of one or more users that suggests a causal relationship between source content and a request for destination content separated from the source content by one or more requests for intermediate content; and determining, based on the behavioral data, content to be placed on a web page. The method may also comprise determining, based on the behavioral data, a location on the web page to place the content. The method may comprise generating the behavioral data by associating a source URL with one or more subsequent URLs that are requested within a predetermined temporal proximity to a request for the source URL. The method may comprise generating the behavioral data by monitoring one or more user pseudonyms comprising a member identifier, a browser identifier, a computing platform identifier, a session identifier, a cookie, an IP address, or a geolocation.