Michael Segundo Ortiz

IR
3papers
Novelty17%
AI Score10

3 Papers

HCJul 24, 2020
Considerations for Eye Tracking Experiments in Information Retrieval

Michael Segundo Ortiz

In this survey I discuss ophthalmic neurophysiology and the experimental considerations that must be made to reduce possible noise in an eye-tracking data stream. I also review the history, experiments, technological benefits and limitations of eye-tracking within the information retrieval field. The concepts of aware and adaptive user interfaces are also explored that humbly make an attempt to synthesize work from the fields of industrial engineering and psychophysiology with information retrieval.

IRDec 5, 2018
Toward Exploratory Search in Biomedicine: Evaluating Document Clusters by MeSH as a Semantic Anchor

Michael Segundo Ortiz, Kazuhiro Seki, Javed Mostafa

The current mode of biomedical literature search is severely limited in effectively finding information relevant to specialists. A potential approach to solving this problem is exploratory search, which allows users to interactively navigate through a vast document collection. As the first step toward exploratory search for specialists in biomedicine, this paper develops a methodology to evaluate quality of document clusters. For this purpose, we incorporate human expertise into data set creation and evaluation framework by leveraging MeSH terms as semantic anchors. In addition, we investigate the benefit of full-text data for improving cluster quality.

IRSep 5, 2018
Measures of Cluster Informativeness for Medical Evidence Aggregation and Dissemination

Michael Segundo Ortiz, Sam Bubnovich, Mengqian Wang et al.

The largest collection of medical evidence in the world is PubMed. However, the significant barrier in accessing and extracting information is information organization. A factor that contributes towards this barrier is managing medical controlled vocabularies that allow us to systematically and consistently organize, index, and search biomedical literature. Additionally, from users' perspective, to ultimately improve access, visualization is likely to play a powerful role. There is a strong link between information organization and information visualization, as many powerful visualizations depend on clustering methods. To improve visualization, therefore, one has to develop concrete and scalable measures for vocabularies used in indexing and their impact on document clustering. The focus of this study is on the development and evaluation of clustering methods. The paper concludes with demonstration of downstream network visualizations and their impact on discovering potentially valuable and latent genetic and molecular associations.