Biomedical text mining applied to document retrieval and semantic indexing

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and relevant terms obtained by the annotation of biological entities (e.g. genes or proteins). This network provides distinct user perspectives and allows navigation over documents with similar terms and is also used to assess document relevance. A network learning procedure, based on previous work from e-mail spam filtering, is proposed, receiving as input a training set of manually classified documents

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well

Influences of Serendipity on Consumer Medical Information Personalization

Serendipity is an important concept in the field of information science. It has a potential of enhancing information seeking process by unexpected discovery. Serendipitous recommendation has been incorporated into the design of personalized systems to minimize blind spots in information delivery. Little evidence has been found to identify how serendipity influences personalization of consumer medical information delivery. This dissertation attempts to examine what roles serendipity plays in filtering consumer medical information and to understand how to incorporate serendipity in an effective manner. In addition, the study seeks to clarify user attitudes on unexpected discoveries of medical content in filtering settings as well as users' interest changes during this process. To empirically analyze the influence of serendipity, a medical news filtering system named MedSDFilter was developed. The system can personalize the delivery of news articles based on users' interest profiles. In MedSDFilter, serendipitous recommendation was integrated into personalized filtering through one of three serendipity models (randomness-based, knowledge-based and learning-based). Using Medical News Today site as information source, three different system modalities were compared by conducting user experiments. Thirty staff members were recruited to read and rate medical news delivered by one of three system modalities. The results of user study indicate that serendipity has an important role in medical news content delivery. As for how to incorporate serendipity, it is shown that using physician knowledge effectively enhanced serendipitous recommendation. In addition, the results suggest that the performance of serendipitous recommendation was further improved after learning algorithms were adopted. This study also provide some evidence to show user satisfaction on unexpected discovery and user interest change associated with this type of discovery. Finally, the study demonstrated the individual difference in seeking consumer medical information. The results of this study provide the system designers implications and suggestions to avoid potential drawbacks related to over-personalization in information delivery. This study enhances the understanding of users' behavior regarding the consumption of medical information and generates new guidelines which can be used in developing information systems in medical area.Doctor of Philosoph