Abstract: Exemplary methods predict the locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information. Predicted locations are used to predict risk of contact, for example of infected persons with others in public spaces.

Abstract: Exemplary methods predict the locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information. Predicted locations are used to predict risk of contact, for example of infected persons with others in public spaces.

Abstract: Disclosed are methods of constructing systems for predicting a subject's medical outcome. Also disclosed are methods and systems for predicting a subject's medical outcome. The disclosed methods and systems can include identifying a group of subjects with the same medical condition and classifying this group of subjects into one or more subgroups. The subgroups are classified based on similarity of medical outcome, using classification and regression trees (CART) to generate a classification tree based on the presence of identifying genetic characteristics. Each node in the classification tree describes the presence of a specific genetic marker and each branch in the classification tree describes a genetic profile that predicts a subject's medical outcome.

Abstract: A sentiment analysis tool receives a t-gram from an electronic device. The t-gram comprises gram(s), each of the gram(s) representing a word in a collection of words. A polarity is set for the t-gram. Possible smaller-gram combinations are generated from the t-gram. Until a condition is met, iterative actions are taken. A likelihood ratio is calculated for the largest of the smaller-gram combinations employing the training set. A determination is made of whether the likelihood ratio meets a minimum replication threshold. If satisfied: the smaller-gram combinations most distant from an undefined polarity value are selected, the smaller-gram combinations employed in calculating the likelihood ratio are excluded; the polarity value for the t-gram is increasing proportional to the likelihood ratio; and the training set is reduced to v-grams that include the t-gram. Otherwise, the size of the smaller-gram is reduced by 1.

Abstract: Disclosed are methods of constructing systems for predicting a subject's medical outcome. Also disclosed are methods and systems for predicting a subject's medical outcome. The disclosed methods and systems can include identifying a group of subjects with the same medical condition and classifying this group of subjects into one or more subgroups. The subgroups are classified based on similarity of medical outcome, using classification and regression trees (CART) to generate a classification tree based on the presence of identifying genetic characteristics. Each node in the classification tree describes the presence of a specific genetic marker and each branch in the classification tree describes a genetic profile that predicts a subject's medical outcome.

Abstract: Disclosed is a medical outcome prediction tool that predicts an individual patient's medical outcomes by identifying patients having a same disease; selecting a set of characteristics unique to an individual; determining the similarities between the individual and other cases; and calculating the expected outcome for the individual that is proportional to a weighted sum of outcomes of similar cases. The similarities can be determined by calculating the number of matches between the individual and cases over the set of characteristics, and using that result to determine a similarity score.

Abstract: An episode classification system including a multitude of diagnosis records. Each of the diagnosis records includes diagnoses information, time of diagnoses information, and patient information. A patient grouper generates at least one patient group by grouping patient records having similar patient information. A diagnosis grouper generates at least one diagnosis group from a patient group by grouping patient records from a patient group that have similar diagnosis information. An episode analyzer includes a probability analyzer, an episode grouper, and a severity analyzer. The probability analyzer performs probability calculations capable of generating a probability value using at least two of the diagnosis records as input entries. The probability value represents the probability that the input entries belong to a single episode. The episode grouper groups diagnosis records determined to belong to a single episode.

Abstract: Disclosed is a medical outcome prediction tool that predicts an individual patient's medical outcomes by identifying patients having a same disease; selecting a set of characteristics unique to an individual; determining the similarities between the individual and other cases; and calculating the expected outcome for the individual that is proportional to a weighted sum of outcomes of similar cases. The similarities can be determined by calculating the number of matches between the individual and cases over the set of characteristics, and using that result to determine a similarity score.

Abstract: An episode classification system including a multitude of diagnosis records is disclosed. Each of the diagnosis records may include diagnoses information, time of diagnoses information, and patient information. A patient grouper may generate at least one patient group by grouping patient records having similar patient information. A diagnosis grouper may generate at least one diagnosis group from a patient group by grouping patient records from a patient group that have similar diagnosis information. An episode analyzer may include a probability analyzer, an episode grouper, and a severity analyzer. The probability analyzer may perform probability calculations capable of generating a probability value using at least two of the diagnosis records as input entries. The probability value may represent the probability that the input entries belong to a single episode. The episode grouper may group diagnosis records determined to belong to a single episode.