Matthew S. Simpson

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The biomedical community makes extensive use of text mining technology. In the past several years, enormous progress has been made in developing tools and methods, and the community has been witness to some exciting developments. Although the state of the community is regularly reviewed, the sheer volume of work related to biomedical text mining and the(More)
Out-of-memory errors are a serious source of unreliability in most embedded systems. Applications run out of main memory because of the frequent difficulty of estimating the memory requirement before deployment, either because it depends on input data, or because certain language features prevent estimation. The typical lack of disks and virtual memory in(More)
Out-of-memory errors are a serious source of unreliability in most embedded systems [22]. Applications run out of main memory because of the frequent difficulty of estimating the memory requirement before deployment, either because it depends on input data, or because certain language features prevent estimation. The typical lack of disks and virtual memory(More)
The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients' cases in electronic health records. This paper presents ways to move beyond(More)
PURPOSE Medical images are a significant information source for clinical decision-making. Currently available information retrieval and decision support systems rely primarily on the text of scientific publications to find evidence in support of clinical information needs. The images and illustrations are available only within the full text of a scientific(More)
Figure 2. Overview of image and text processing steps for creating enriched citations. In the context of this work, an " image " includes not only biomedical images, such as CT, MRI, X-ray, and other modalities, but also illustrations, figures, charts, graphs and other visual material appearing in biomedical journals, electronic health records, and Figure(More)
This article describes the participation of the Image and Text Integration (ITI) group from the United States National Library of Medicine (NLM) in the ImageCLEF 2009 medical retrieval track. Our methods encompass a variety of techniques relating to document summarization and text-and content-based image retrieval. Our text-based approach utilizes the(More)
SUMMARY Memory access violations are a leading source of unreliability in C programs. As evidence of this problem, a variety of methods exist that retrofit C with software checks to detect memory errors at runtime. However, these methods generally suffer from one or more drawbacks including the inability to detect all errors, the use of incompatible(More)
Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image)(More)
Images are frequently used in articles to convey essential information in context with correlated text. However, searching images in a task-specific way poses significant challenges. To minimize limitations of low-level feature representations in content-based image retrieval (CBIR), and to complement text-based search, we propose a multi-modal image search(More)