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KA-Recsys: Patient Focused Knowledge Appropriate Health Recommender System

Published: 07 July 2022 Publication History

Abstract

Chronic disease patients, such as diabetics, cancer patients, and heart disease patients, actively seek health information for self-management and decision-making every single day. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients' changing needs, assists them with easy information accessibility. Nevertheless, patients' needs become more complex with disease progression and their increased knowledge about disease. Hence, a unique requirement of the PHRS would be to suggest health information in line with patients' changing knowledge about the disease. However, current PHRS are personalized to patient interest and don't consider their knowledge about disease. By providing patients with information tailored at their knowledge-level, they not only are more likely to understand and engage better in disease management, but can use PHRS for disease related learning. Hence, the overarching goal of my PhD thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients' dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS (KA-PHRS ). A critical innovation of KA-PHRS is the patient knowledge model that keeps track of patients' changing knowledge-level about disease and enables knowledge-appropriate recommendations. The expectation is that health information suggested by KA-PHRS will increase as well as benefit patients' involvement in self management and treatment.Chronic disease patients, such as diabetics, cancer patients, and heart disease patients, actively seek health information for self-management and decision-making every single day. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients' changing needs, assists them with easy information accessibility. Nevertheless, patients' needs become more complex with disease progression and their increased knowledge about disease. Hence, a unique requirement of the PHRS would be to suggest health information in line with patients' changing knowledge about the disease. However, current PHRS are personalized to patient interest and don't consider their knowledge about disease. By providing patients with information tailored at their knowledge-level, they not only are more likely to understand and engage better in disease management, but can use PHRS for disease related learning. Hence, the overarching goal of my PhD thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients' dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS (KA-PHRS ). A critical innovation of KA-PHRS is the patient knowledge model that keeps track of patients' changing knowledge-level about disease and enables knowledge-appropriate recommendations. The expectation is that health information suggested by KA-PHRS will increase as well as benefit patients' involvement in self management and treatment.

References

[1]
Robin De Croon, Leen Van Houdt, Nyi Nyi Htun, Gregor vStiglic, Vero Vanden Abeele, and Katrien Verbert. 2021. Health Recommender Systems: Systematic Review. Journal of Medical Internet Research, Vol. 23 (2021).
[2]
Vaartio-Rajalin Heli, Leino-Kilpi Helena, Iire Liisa, Lehtonen Kimmo, and Minn Heikki. 2015. Oncologic Patients' Knowledge Expectations and Cognitive Capacities During Illness Trajectory: Analysis of Critical Moments and Factors. Holistic Nursing Practice, Vol. 29 (2015), 232--244.
[3]
J. Tariman, D. Berry, B. Cochrane, A. Doorenbos, and K. Schepp. 2012. Physician, patient, and contextual factors affecting treatment decisions in older adults with cancer and models of decision making: a literature review. Oncology nursing forum, Vol. 39 1 (2012), E70--83.
[4]
Khushboo Thaker, Yun Huang, Peter Brusilovsky, and He Daqing. 2018. Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. In Proc. of the 11th Int. Conf. on EDM. IEDMS, Buffalo, NY, USA, 592--592.
[5]
Khushboo Thaker, Lei Zhang, Daqing He, and Peter Brusilovsky. 2020. Recommending Remedial Readings Using Student's Knowledge state. In Proc. of the 13th Int. Conf. on EDM. EDM, Virtual, 233--244.
[6]
Yu Chi, Susan Birkhoff, Daqing He, Heidi Donovan, Leah Rosenblum, Peter Brusilovsky, Vivian Hui, and Young Ji Lee. 2022. Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community. JMIR Cancer, Vol. 8, 2 (12 Apr 2022), e33110. https://doi.org/10.2196/33110

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
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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Published: 07 July 2022

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  1. domain knowledge
  2. health recommender
  3. knowledge modeling

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