Abstract This article presents our steps to integrate complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated faceted search tool, accompanied by the visualisation of results of automatic information extraction from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals. Our exemplary use cases are nephrology and mammography. The software was first developed in the nephrology domain and then adapted to the mammography use case. We report on these case studies, illustrating how the application can be used by a clinician and which questions can be answered. We show that our architecture and the employed software modules are suitable for both areas of application with a limited amount of adaptations. For example, in nephrology we try to answer questions about the temporal characteristics of event sequences to gain significant insight from the data for cohort selection. We present a versatile time-line tool that enables the user to explore relations between a multitude of diagnosis and laboratory values.
Faceted search allows the user to explore a data collection by applying filters in an arbitrary order. The information elements are organised by a faceted classification system among multiple dimensions, called facets. In a faceted search interface, labels are assigned to hierarchical items from the collection. This representation known as hierarchical faceted search is gaining great attraction within several information retrieval communities @cite_13 @cite_11 . Some previous approaches focus on automating the creation of such hierarchical faceted metadata structures @cite_22 . We however implement a special multi-facet functionality for our medical data where we already know the hierarchy of the terms according to clinical ontologies. Design recommendations for hierarchical faceted search interfaces @cite_12 include the support for flexible navigation, seamless integration with keyword search on the full text, fluid alternation between refining and expanding, avoidance of empty result sets, and at all times retaining a feeling of control and understanding (of the patient data).
@cite_13 @cite_22 @cite_12 @cite_11
2118322263 11540594 1995304706
There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90 of the participants preferred the metadata approach overall, 97 said that it helped them learn more about the collection, 75 found it more flexible, and 72 found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections. We describe Castanet, an algorithm for automatically generating hierarchical faceted metadata from textual descriptions of items, to be incorporated into browsing and navigation interfaces for large information collections. From an existing lexical database (such as WordNet), Castanet carves out a structure that reflects the contents of the target information collection; moderate manual modifications improve the outcome. The algorithm is simple yet effective: a study conducted with 34 information architects finds that Castanet achieves higher quality results than other automated category creation algorithms, and 85 of the study participants said they would like to use the system for their work. This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved