Biomedical subjects

Qinghua Zou

Publications and source records attributed to Qinghua Zou.

3 recordsLinked to original sources

PubMed2003

Workflow management of HIS/RIS textual documents with PACS image studies for neuroradiology.

Reviewing brain tumor patients' complete medical record is a daunting task for any clinician. In current practice, the radiologist examines the most recent documents and then dictates an assessment of the patient's condition based on a review of the most current imaging study and compared with the most recent previous image study. Occasionally, the radiologist searches other clinical documents when more precise detail is needed. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information in three distinct ways: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.

Algorithms↗

PubMed2003

IndexFinder: a method of extracting key concepts from clinical texts for indexing.

Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.

Abstracting and Indexing↗

PubMed2002

Modeling medical content for automated summarization.

Medical information is available from a variety of new online resources. Given the number and diversity of sources, methods must be found that will enable users to quickly assimilate and determine the content of a document. Summarization is one such tool that can help users to quickly determine the main points of a document. Previous methods to automatically summarize text documents typically do not attempt to infer or define the content of a document. Rather these systems rely on secondary features or clues that may point to content. This paper describes text summarization techniques that enable users to focus on the key content of a document. The techniques presented here analyze groups of similar documents in order to form a content model. The content model is used to select sentences forming the summary. The technique does not require additional knowledge sources; thus the method should be applicable to any set of text documents.

Automation↗