Number of co-authors:12
Number of publications with 3 favourite co-authors:Shuguang Wang:2Kirk Pruhs:1Dave Krebs:1
Milos Hauskrecht's 3 most productive colleagues in number of publications:Jingtao Wang:8Panos K. Chrysanth..:6Josť Carlos Brusto..:5
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Publications by Milos Hauskrecht (bibliography)
Krebs, Dave, Conrad, Alexander, Hauskrecht, Milos and Wang, Jingtao (2011): MARBLS: a visual environment for building clinical alert rules. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 67-68.
Physicians and nurses usually rely on hospital information systems (HIS) for detecting a variety of adverse clinical conditions and reminding repetitive treatments. However, the acquisition of alert rules expected by HIS from experts remains a challenging, error-prone, and time-consuming process. In this work, we present MARBLS (Medical Alert Rule BuiLding System) -- a visual environment to facilitate the design and definition of clinical alert rules. MARBLS enables a two-way, synchronized visual rule workspace and visual query explorer. Monitoring rules can be built by manipulating block components in the rule workspace, by querying and generalizing region of interests in the visual query explorer via direct manipulations, or a combination of both. Informal testing with doctors has shown positive feedback.
© All rights reserved Krebs et al. and/or ACM Press
Wang, Shuguang and Hauskrecht, Milos (2010): Effective query expansion with the resistance distance based term similarity metric. In: Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2010. pp. 715-716.
In this paper, we define a new query expansion method that relies on term similarity metric derived from the electric resistance network. This proposed metric lets us measure the mutual relevancy in between terms and between their groups. This paper shows how to define this metric automatically from the document collection, and then apply it in query expansion for document retrieval tasks. The experiments show this method can be used to find good expansion terms of search queries and improve document retrieval performance on two TREC genomic track datasets.
© All rights reserved Wang and Hauskrecht and/or their publisher
Wang, Shuguang and Hauskrecht, Milos (2008): Improving biomedical document retrieval using domain knowledge. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008. pp. 785-786.
Research articles typically introduce new results or findings and relate them to knowledge entities of immediate relevance. However, a large body of context knowledge related to the results is often not explicitly mentioned in the article. To overcome this limitation the state-of-the-art information retrieval approaches rely on the latent semantic analysis in which terms in articles are projected to a lower dimensional latent space and best possible matches in this space are identified. However, this approach may not perform well enough if the number of explicit knowledge entities in the articles is too small compared to the amount of knowledge in the domain. We address the problem by exploiting a domain knowledge layer, a rich network of relations among knowledge entities in the domain extracted from a large corpus of documents. The knowledge layer supplies the context knowledge that lets us relate different knowledge entities and hence improve the information retrieval performance. We develop and study a new framework for i) learning and aggregating the relations in the knowledge layer from the literature corpus; ii) and for exploiting these relations to improve the information-retrieval of relevant documents.
© All rights reserved Wang and Hauskrecht and/or ACM Press
Mossť, Daniel, Comfort, Louise, Amer, Ahmed, Brustoloni, Josť Carlos, Chrysanthis, Panos K., Hauskrecht, Milos, Labrinidis, Alexandros, Melhem, Rami G. and Pruhs, Kirk (2006): Secure-CITI Critical Information-Technology Infrastructure. In: Fortes, Josť A. B. and MacIntosh, Ann (eds.) DG.O 2006 - Proceedings of the 7th Annual International Conference on Digital Government Research May 21-24, 2006, San Diego, California, USA. pp. 253-254.
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