890 resultados para Personalized medicine
Resumo:
Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.
Resumo:
Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.
Resumo:
As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.
Resumo:
Over the last century, environmental and occupational medicine has played a significant role in the protection and improvement of public health. However, scientific integrity in this field has been increasingly threatened by pressure from some industries and governments. For example, it has been reported that the tobacco industry manipulated eminent scientists to legitimise their industrial positions, irresponsibly distorted risk and deliberately subverted scientific processes, and influenced many organisations in receipt of tobacco funding. Many environmental whistleblowers were sued and encountered numerous personal attacks. In some countries, scientific findings have been suppressed and distorted, and scientific advisory committees manipulated for political purposes by government agencies. How to respond to these threats is an important challenge for environmental and occupational medicine professionals and their societies. The authors recommend that professional organisations adopt a code of ethics that requires openness from public health professionals; that they not undertake research or use data where they do not have freedom to publish their results if these data have public health implications; that they disclose all possible conflicts; that the veracity of their research results should not be compromised; and that their research independence be protected through professional and legal support. The authors furthermore recommend that research funding for public health not be directly from the industry to the researcher. An independent, intermediate funding scheme should be established to ensure that there is no pressure to analyse data and publish results in bad faith. Such a funding system should also provide equal competition for funds and selection of the best proposals according to standard scientific criteria.
Resumo:
The existing Collaborative Filtering (CF) technique that has been widely applied by e-commerce sites requires a large amount of ratings data to make meaningful recommendations. It is not directly applicable for recommending products that are not frequently purchased by users, such as cars and houses, as it is difficult to collect rating data for such products from the users. Many of the e-commerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended to the user. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their online navigation behaviour. This paper proposes to integrate collaborative filtering and search-based techniques to provide personalized recommendations for infrequently purchased products. Two different techniques are proposed, namely CFRRobin and CFAg Query. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAg Query technique uses the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAg Query perform better than the standard Collaborative Filtering (CF) and the Basic Search (BS) approaches, which are widely applied by the current e-commerce applications. The CFRRobin and CFAg Query approaches also outperform the e- isting query expansion (QE) technique that was proposed for recommending infrequently purchased products.
Resumo:
It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.
Resumo:
With the world’s largest population of 1.3 billion, China is a rapidly developing country. In line with this development, China’s enormous health system is experiencing an unprecedented series of reforms. According to a recent official government report, China has 300, 000 health organizations, which include 60, 000 hospitals and a total number of 3.07 million beds (China NBoSoP 2006). To provide health services for the national population, as well as the substantial number of visitors, China has 1.93 million doctors and 1.34 million registered nurses (China NBoSoP 2006). From 1984 to 2004, the number of inpatients grew from about 25 to 50 million, with outpatient figures increasing from 1.1 to 1.3 billion (China MoH 2006). The scale of the health system is likely bigger than in any other countries in the world, but the quality of medical services is still among the levels of developing countries. In 2005, approximately 3.8% of inpatients (about 1.5 million)(China NBoSoP 2006) were admitted because of injury and poisoning, which created significant load for the acute health system. These increased figures are at least partly because of the development of the health system and technological health-care advances but, even with such advances, this rapid change in emergency health-care demand has created a very significant burden on existing systems...
Resumo:
This article places the 6 June 2012 transit of Venus in the context of James Cook’s voyage from England to the South Pacific to observe the 1769 transit of Venus. A description is given on how to use a computer program called Stellarium to ‘observe’ the 1769 transit of Venus exactly as Cook saw it from the island of Tahiti in the South Pacific.
Resumo:
The development of any new profession is dependent on the development of a special body of knowledge which is the domain of the profession and key to this is the conduct of research. In 2007, as part of the settlement of an Enterprise Bargaining Agreement and following sustained lobbying by Emergency Physicians, the Queensland Government agreed to establish an Emergency Medicine Research Fund to foster the development of research activities in Emergency Medicine in Queensland. That fund is now managed by the Queensland Emergency Medicine Research Foundation. The aims of this article are to describe the strategic approaches taken by the Foundation and its first three years of experience, to describe the application of research funds and to foreshadow an evaluative framework for determining the strategic value of this community investment. The Foundation has developed a range of personnel and project support funding programs and competition for funding has increased. Ongoing evaluation will seek to determine the effectiveness of this funding strategy on improving the effectiveness of research performance and the clinical and organisational outcomes that may derive from that initiative.
Resumo:
Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theorectical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine.
Resumo:
Diet and medical treatment are the standard treatment for type 2 diabetes. In obese subjects with type 2 diabetes, bariatric surgery is effective in resolving diabetes. Two clinical trials comparing bariatric surgery to medical treatment were evaluated. Both the Surgical Treatment And Medications Potentially Eradicate Diabetes Efficiently (STAMPEDE) trial (laparoscopic Roux-En Y gastric bypass and sleeve gastrectomy) and the DIet and medical therapy versus BAriatric SurgerY in type 2 diabetes (DIBASY) trial (laparoscopic gastric bypass and biliopancreatic-diversion) showed that surgery was more effective than medical care in resolving or managing type 2 diabetes. Larger studies, or a compilation of studies, are needed to determine whether one of these procedures is better, or if they are all similarly effective, and this should also be weighed against the risk of the operations.