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Context-sensitive, personalized search at the point of care

Published: 20 June 2022 Publication History

Abstract

Medical data science opportunities emerging in recent years have enabled the retrieval of similar cases, related treatments, and supportive information. However, current medical domain search engines, such as PubMed, continue to retrieve health documents in a simple similarity form with the users' situational and contextual features not well integrated. Meaning, current medical information systems are neither able to consider the case context nor do personalization, thus requiring too much time the practitioners don't have.

References

[1]
Hélder Antunes and Carla Teixeira Lopes. 2020. Proposal and comparison of health specific features for the automatic assessment of readability. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1973--1976.
[2]
Sameh Frihat and Norbert Fuhr. 2021. Trec 2021 clinical trials retrieval, duisburgessen university submission.
[3]
Norbert Fuhr et al. 2018. An information nutritional label for online documents. In ACM SIGIR Forum number 3. Vol. 51. ACM New York, NY, USA, 46--66.
[4]
Peter Ingwersen and Kalervo Järvelin. 2006. The turn: Integration of information seeking and retrieval in context. Vol. 18. Springer Science & Business Media.
[5]
Alistair EW Johnson et al. 2016. Mimic-iii, a freely accessible critical care database. Scientific data, 3, 1, 1--9.
[6]
Hyeoneui Kim, Sergey Goryachev, Graciela Rosemblat, Allen Browne, Alla Keselman, and Qing Zeng-Treitler. 2007. Beyond surface characteristics: a new health text-specific readability measurement. In AMIA Annual Symposium Proceedings. Vol. 2007. American Medical Informatics Association, 418.
[7]
Sascha Kriewel and Norbert Fuhr. 2010. Evaluation of an adaptive search suggestion system. In European Conference on Information Retrieval. Springer, 544--555.
[8]
Carolyn E Lipscomb. 2000. Medical subject headings (mesh). Bulletin of the Medical Library Association, 88, 3, 265.
[9]
Carla Teixeira Lopes. 2009. Context-based health information retrieval. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 845--845.
[10]
Lydia O'Sullivan, Prasanth Sukumar, Rachel Crowley, Eilish McAuliffe, and Peter Doran. 2020. Readability and understandability of clinical research patient information leaflets and consent forms in ireland and the uk: a retrospective quantitative analysis. BMJ open, 10, 9, e037994.
[11]
David L Sackett, William MC Rosenberg, JA Muir Gray, R Brian Haynes, and W Scott Richardson. 1996. Evidence based medicine: what it is and what it isn't. (1996).
[12]
Sarah E Schwarm and Mari Ostendorf. 2005. Reading level assessment using support vector machines and statistical language models. In Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL'05), 523--530.

Cited By

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  • (2023)Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search EnginesApplied Sciences10.3390/app13191061213:19(10612)Online publication date: 23-Sep-2023

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      cover image ACM Conferences
      JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries
      June 2022
      392 pages
      ISBN:9781450393454
      DOI:10.1145/3529372
      • General Chairs:
      • Akiko Aizawa,
      • Thomas Mandl,
      • Zeljko Carevic,
      • Program Chairs:
      • Annika Hinze,
      • Philipp Mayr,
      • Philipp Schaer
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Publication History

      Published: 20 June 2022

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      Author Tags

      1. contextual information retrieval
      2. medical information retrieval
      3. medical information systems
      4. personalized search

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      JCDL '22 Paper Acceptance Rate 35 of 132 submissions, 27%;
      Overall Acceptance Rate 415 of 1,482 submissions, 28%

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      • (2023)Document Difficulty Aspects for Medical Practitioners: Enhancing Information Retrieval in Personalized Search EnginesApplied Sciences10.3390/app13191061213:19(10612)Online publication date: 23-Sep-2023

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