With advances in natural language processing (NLP), machine learning (ML) and artificial intelligence (AI), there are new opportunities for improving findability among existing public-facing resources. This project seeks to inform findability, especially for multiple chronic condition (MCC) resources, by describing current search capabilities and limitations across several of AHRQ’s publicly available domains and by identifying and piloting a novel NLP/ML approach to make suggested improvements. This work intentionally engages with the overlap of numerous disciplines including information extraction, information retrieval, data and text mining, knowledge management, and best practices in health care. We are looking to apply this work across …
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Situated at the intersection of people, technology, and information, the College of Information's faculty, staff and students invest in innovative research, collaborative partnerships, and student-centered education to serve a global information society. The college offers programs of study in information science, learning technologies, and linguistics.
With advances in natural language processing (NLP), machine learning (ML) and artificial intelligence (AI), there are new opportunities for improving findability among existing public-facing resources. This project seeks to inform findability, especially for multiple chronic condition (MCC) resources, by describing current search capabilities and limitations across several of AHRQ’s publicly available domains and by identifying and piloting a novel NLP/ML approach to make suggested improvements. This work intentionally engages with the overlap of numerous disciplines including information extraction, information retrieval, data and text mining, knowledge management, and best practices in health care. We are looking to apply this work across all domains but will start by focusing on specific AHRQ domains. Given limited API access, we scraped the content of digital.ahrq.gov and the patient centered medical home (PCMH) resources and performed automated search using a set of related terms that align with an MCC scenario: hypertension, osteoarthritis, and chronic kidney disease. We obtained results confirming the limitations of existing search.
Physical Description
1 p.
Notes
The International Conference on Knowledge Management (ICKM) provides researchers and practitioners from all over the world a forum for discussion and exchange of ideas concerning theoretical and practical aspects of Knowledge Management. ICKM 2020 held December 3-5 as a virtual meeting using Cisco WebEx (technical support from NC Central University, Durham, NC) and hosted by the Knowledge and Information Professional Association (KIPA). The conference theme is “Knowledge Commons in the City of Medicine,” covering the institutional analysis of data and the development of knowledge sharing in any subject area.
16th International Conference on Knowledge Management (ICKM-2020), December 3-5, 2020. Virtual meeting using Cisco WebEx (technical support from NC Central University), Durham, NC, United States,
This poster is part of the following collection of related materials.
International Conference on Knowledge Management (ICKM)
Serving as digital proceedings, this collection includes papers, posters, and slides from invited talks as well as practitioner and sponsor presentations for the annual International Conference on Knowledge Management (ICKM).
Marcial, Laura Haak; Santini, Silas; Kery, Caroline; Brown, Stephen; Chew, Rob & Blumenfeld, Barry.Using Query Expansion to Improve Findability of Resources Addressing Multiple Chronic Conditions,
poster,
December 2020;
(https://digital.library.unt.edu/ark:/67531/metadc1813464/:
accessed October 2, 2023),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Information.
