Quality-Based Knowledge Discovery from Medical Text on the Web

The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. Consequently there is need for techniques which enable the quality-based discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining helps to cope with the interpretation of these large volumes of data. Co-occurrence analysis is a technique applied in text mining. Statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a selection of quality-oriented Web-based tools for analyzing biomedical literature, and specifically discuss PolySearch, FACTA and Kleio. Finally we discuss Pointwise Mutual Information (PMI), which is a measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making and quality-enhancing.