Plachouras
Vasileios Plachouras, London GB
Patent application number | Description | Published |
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20160092793 | PHARMACOVIGILANCE SYSTEMS AND METHODS UTILIZING CASCADING FILTERS AND MACHINE LEARNING MODELS TO CLASSIFY AND DISCERN PHARMACEUTICAL TRENDS FROM SOCIAL MEDIA POSTS - Systems and methods for utilizing filters to reduce an incoming stream of textual messages to a smaller subset of potentially relevant textual messages, and using trained machine learning models to analyze and classify the content of such textual messages. Analyzed messages that belong to a relevant class as determined by the machine learning model are stored in a database, giving users the ability to determine and analyze trends from the subset of messages, such as adverse side effects caused by pharmaceuticals or the efficacy of pharmaceuticals. Relationships between the side effects caused by different pharmaceuticals can be used to predict potential candidates for drug repositioning. | 03-31-2016 |
Vassilis Plachouras, Chalandri GR
Patent application number | Description | Published |
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20110087684 | POSTING LIST INTERSECTION PARALLELISM IN QUERY PROCESSING - Disclosed herein is parallel processing of a query, which uses inter-query parallelism in posting list intersections. A plurality of tasks, e.g., posting list intersection tasks, are identified for processing in parallel by a plurality of processing units, e.g., a plurality of processing cores of a multi-core system. | 04-14-2011 |
Vassilis Plachouras, Barcelona Catalunya ES
Patent application number | Description | Published |
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20090282014 | Systems and Methods for Predicting a Degree of Relevance Between Digital Ads and a Search Query - Systems and methods for predicting a degree of relevance between a set of candidate digital ads and a search query are disclosed. Generally, an ad provider receives a digital ad request associated with a search query. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the search query associated with the digital ad request, and determines a degree of relevance between the set of candidate digital ads and the search query based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the search query, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request. | 11-12-2009 |
20090282016 | Systems and Methods for Building a Prediction Model to Predict a Degree of Relevance Between Digital Ads and a Search Query or Webpage Content - Systems and methods for building a prediction model to predict a degree of relevance between digital ads and a search query or webpage content are disclosed. Generally, an indication of relevance is received between a plurality of digital ads and one of a webpage content or a search query. A set of features is extracted from the plurality of digital ads and one of the webpage content or the search query. A prediction model is then built to predict a degree of relevance between the set of candidate digital ads and one of a second webpage content or a second search query, where the prediction model is built based at least one the received indication of relevance and the extracted set of features. | 11-12-2009 |
Vassillis Plachouras, Barcelona Catalunya ES
Patent application number | Description | Published |
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20090282015 | Systems and Methods for Predicting a Degree of Relevance Between Digital Ads and Webpage Content - Systems and methods for predicting a degree of relevance between a set of candidate digital ads and webpage content are disclosed. Generally, an ad provider receives a digital ad request associated with webpage content. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the webpage content, and determines a degree of relevance between the set of candidate digital ads and the webpage content based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the webpage content, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request. | 11-12-2009 |