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Literature survey Yaliang Li, Chaochun Liu performed early work on The medical crowdsourced question answering (QA) websites are booming in recent years, and increasingly large amount of patients and doctors are involved. The valuable information from these medical crowd sourced QA websites can benefit patients, doctors and the society. One key to unleash the power of these QA websites is to extract medical knowledge from the noisy question-answer pairs and filter out unrelated or even incorrect information. Xiaoxin Yin, Jiawei Han.
The world-wide web has become the most important information source for most of us. Unfortunately, there is no guarantee for the correctness of information on the web. Moreover, different web sites often provide conflicting in- formation on a subject, such as different specifications for the same product. In this paper, we propose a new problem called Veracity, i.e., conformity to truth, which studies how to find true facts from a large amount of conflicting information on many subjects that is provided by various web sites. We design a general framework for the Veracity problem, and invent an algorithm called Truth Finder, which utilizes the relationships between web sites and their information, i.e., a web site is trustworthy if it provides many pieces of true information, and a piece of information is likely to be true if it is provided by many trustworthy web sites. Our ex- pediments’ show that Truth Finder successfully finds true facts among conflicting information, and identifies trustworthy web sites better than the popular search engines.
Paresh Karande. Health is one of the increasing subjects used for assessing health condition among patients who suffer from specific ailment or diseases. It has been assumed that identification of the variables is able to mirror the one’s overall health conditions. We aims to model the relationship between Health variables using integrated model of inference system and linear regression. Linguistic data were collected by a guided interview and fed into the deep sparse inference system to yield Health indices. We next propose a novel profound learning plan to surmise the conceivable maladies given the inquiries of well-being seekers.
The proposed plan is embodied two key parts. The main part mines the discriminant therapeutic marks from crude elements. The second esteems the crude components and their marks as info hubs in one layer and concealed hubs in the consequent layer, individually. In the interim, it takes in the between relations between these two layers by means of pre-preparing with pseudo-marked information. Taking after that, the shrouded hubs serve as crude components for the more unique mark mining. With incremental and option rehashing of these two segments, our plan manufactures a scantily joined profound construction modelling with three shrouded layersLiqiang Nie. A national survey conducted by the Pew Research Center1 in Jan 2013, where they reported that one in three American adults have gone online to figure out their medical conditions in the past 12 months from the report time. To better cater to health seekers, a growing number of community-based healthcare services have turned up, including HealthTap2, HaoDF3 and WebMD4.
They are disseminating personalized health knowledge and connecting patients with doctors worldwide via question answering. These forums are very attractive to both professionals and health seekers. For professionals, they are able to increase their reputations among their colleagues and patients, strengthen their practical knowledge from interactions with other renowned doctors, as well as possibly attract more new patients.
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