skip to main content
10.1145/3404835.3462792acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Precision Medicine Search for Paediatric Oncology

Published:11 July 2021Publication History

ABSTRACT

We present a search engine aimed to help clinicians find targeted treatments for children with cancer. Childhood cancer is a leading cause of death and clinicians increasingly seek treatments that are tailored to an individual patient, particularly their tumour genetics. Finding treatments that are specific to paediatrics and match individual genetics is a real challenge amongst the vast and growing body of medical literature and clinical trials. We aim to help clinicians through a search system tailored to this problem.

The system retrieves PubMed articles and clinical trials. Entity extraction is done to highlight genes, drugs and cancers --- three key information types clinicians care about. Query suggestion helps clinicians formulate otherwise difficult queries and results are presented as a knowledge graph to help result interpretability. The proposed system aims to both significantly reduce the effort of searching for targeted treatments and potentially find life saving treatments that may have otherwise been missed. Demo details at http://health-search.csiro.au/oscar/

References

  1. Euan A Ashley. 2016. Towards precision medicine. Nature Reviews Genetics, Vol. 17, 9 (2016), 507.Google ScholarGoogle ScholarCross RefCross Ref
  2. Seth Carbon, Eric Douglass, Benjamin M Good, Deepak R Unni, Nomi L Harris, Christopher J Mungall, Siddartha Basu, Rex L Chisholm, Robert J Dodson, Eric Hartline, et al. 2021. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research, Vol. 49, 1 (2021), 325--334.Google ScholarGoogle ScholarCross RefCross Ref
  3. Maria Gonzalez, Matteo S. Carlino, Robert Richard Zielinski, Joel Smith, Robyn Saw, Angela Hong, Monika Keczkowska, Roslyn Ristuccia, Jim McBride, Alexander M. Menzies, and Georgina V. Long. 2016. An app to increase cross-referral and recruitment to melanoma clinical trials. Journal of Clinical Oncology, Vol. 34 (2016), 9590--9590.Google ScholarGoogle ScholarCross RefCross Ref
  4. Ben He and Iadh Ounis. 2006. Query performance prediction. Information Systems, Vol. 31, 7 (2006), 585--594.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Thomas Isaac, Jie Zheng, and Ashish Jha. 2012. Use of UpToDate and outcomes in US hospitals. Journal of hospital medicine, Vol. 7, 2 (2012), 85--90.Google ScholarGoogle ScholarCross RefCross Ref
  6. Donghyeon Kim, Jinhyuk Lee, Chan Ho So, Hwisang Jeon, Minbyul Jeong, Yonghwa Choi, Wonjin Yoon, Mujeen Sung, and Jaewoo Kang. 2019. A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining. IEEE Access, Vol. 7 (2019), 73729--73740.Google ScholarGoogle ScholarCross RefCross Ref
  7. Bevan Koopman and Guido Zuccon. 2014. Why Assessing Relevance in Medical IR is Demanding. In MedIR at SIGIR .Google ScholarGoogle Scholar
  8. Bevan Koopman, Guido Zuccon, and Peter Bruza. 2017. What Makes an Effective Clinical Query and Querier? Journal of the Association for Information Science and Technology, Vol. 68, 11 (2017), 2557--2571.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Catherine G Lam, Scott C Howard, Eric Bouffet, and Kathy Pritchard-Jones. 2019. Science and health for all children with cancer. Science, Vol. 363, 6432 (2019), 1182--1186.Google ScholarGoogle Scholar
  10. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, Vol. 36, 4 (2020), 1234--1240.Google ScholarGoogle ScholarCross RefCross Ref
  11. Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018. Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction. In Conference on Empirical Methods in Natural Language Processing. 3219--3232.Google ScholarGoogle ScholarCross RefCross Ref
  12. Federica Saletta, Luciano Dalla Pozza, and Jennifer A Byrne. 2015. Genetic causes of cancer predisposition in children and adolescents. Translational pediatrics, Vol. 4, 2 (2015), 67.Google ScholarGoogle Scholar
  13. Harrisen Scells and Guido Zuccon. 2018. Searchrefiner: A Query Visualisation and Understanding Tool for Systematic Reviews. In Conference on Information and Knowledge Management (CIKM). 1939--1942.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Logan G Spector, Nathan Pankratz, and Erin L Marcotte. 2015. Genetic and nongenetic risk factors for childhood cancer. Pediatric Clinics, Vol. 62, 1 (2015), 11--25.Google ScholarGoogle Scholar
  15. Eva Steliarova-Foucher, Murielle Colombet, Lynn AG Ries, Florencia Moreno, Anastasia Dolya, Freddie Bray, Peter Hesseling, Hee Young Shin, Charles A Stiller, S Bouzbid, et al. 2017. International incidence of childhood cancer, 2001--10: a population-based registry study. The Lancet Oncology, Vol. 18, 6 (2017), 719--731.Google ScholarGoogle ScholarCross RefCross Ref
  16. Judith Trotman, Xavier Badoux, Admir Huseincehajic, Michele Gambrill, Anais LeGall, Michelle Daly, Mark Lacey, Sonia Byrne, Jennifer Aung, Shashi Nair, et al. 2013. Clintrial Refer-a Mobile App To Connect Patients With Local Clinical Trials. Blood, Vol. 122, 21 (2013).Google ScholarGoogle Scholar
  17. Anton van der Vegt, Guido Zuccon, and Bevan Koopman. 2020. Do better search engines really equate to better clinical decisions? If not, why not? Journal of the Association for Information Science and Technology, Vol. 72, 2 (2020), 141--155.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jinghui Zhang, Michael F Walsh, Gang Wu, Michael N Edmonson, Tanja A Gruber, John Easton, Dale Hedges, Xiaotu Ma, Xin Zhou, Donald A Yergeau, et al. 2015. Germline mutations in predisposition genes in pediatric cancer. New England Journal of Medicine, Vol. 373, 24 (2015), 2336--2346.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Precision Medicine Search for Paediatric Oncology

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
      July 2021
      2998 pages
      ISBN:9781450380379
      DOI:10.1145/3404835

      Copyright © 2021 ACM

      Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 July 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      Overall Acceptance Rate792of3,983submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader