SocialLDA:Scalable Topic Modeling in Social Networks

Thesis (Master's)--University of Washington, 2012Topical categorization of blogs, documents or other objects that can be tagged with text, improves the experience for end users. Latent Dirichlet allocation (LDA) is a well studied algorithm that discovers latent topics from a corpus of documents so that the documents can then be assigned automatically into appropriate topics. New documents can also be classified into topics based on these latent topics. However, when the set of documents is very large and varies significantly from user to user, the task of calculating a single global LDA topic model, or an individual topic model for each and every user can become very expensive in large scale internet settings. The problem is further compounded by the need to periodically update this model to keep up with the relatively dynamic nature of data in online social networks such as Facebook, Twitter, and FriendFeed. In this work we show that the computation cost of using LDA for a large number of users connected via a social network can be reduced without compromising the quality of the LDA model by taking into account the social connections among the users in the network. Instead of a single global model based on every document in the network we propose to use a model created from messages that are authored by and received by a fixed number of most influential users. We use PageRank as the influence measure and show that this Social LDA model provides an effective model to use as it reduces the number of documents to process thereby reducing the cost of computing the LDA. Such a model can be used both for categorizing a users incoming document stream as well as finding user interest based on the user's authored documents. Further this also helps in the cold start problem where a model based on a user's own messages is insufficient to create a good LDA model

Targeted temperature management in neurocritical care: Boon or bust

Temperature management in neurocritical care is important and has gained due importance in recent years. Targeted temperature management (TTM) includes deliberate reduction of core body temperature to a range of 32–34°C in comatose survivors of cardiac arrest after return of spontaneous circulation and also therapeutic hypothermia (33–35°C) in normothermic patients or controlled normothermia (cooling patients to 37°C) in hyperthermic patients. This review focuses on the practical aspects of cooling and temperature management in neurocritical care. An electronic search of Google Scholar, MEDLINE (OVID), EMBASE and the Cochrane library was performed. The physiological changes, potential side effects along with significant research and clinical applications in the field of temperature management are main highlights. Currently, the research evidence is available for TTM in adults following cardiac arrest, neonatal hypoxic ischaemic encephalopathy and raised intracranial pressure. Controlled normothermia in neurocritical care population is rapidly gaining popularity. However, data on disease specific therapeutic markers, therapeutic depth and duration and prognostication of this modality are limited. Overall in experienced hands, the technique is safe to execute without the major morbidity and a potential to improve the outcome. Though over the years, clinical applications are unable to consistently demonstrate its benefits, but TTM in neurological insult has become an established and promising concept