Geospatial recommender system for the location of health services

Avila-Vazquez, Daniela; Martínez, María López; Maya, Dafne; Olvera, Xochitl; Guzmán, Giovanni; Torres, Miguel; Quintero, Rolando; Quintana, Ismael

doi:10.1109/cisti.2014.6877023

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Knowledge-based techniques are often used to incorporate additional information of patients into the recommendation process [ 112 ] and have been shown to improve the quality of recommendations while alleviating other drawbacks such as cold-start and sparsity issues [ 14 ]. Some studies use straightforward approaches, such as if-else reasoning based on domain knowledge [ 9 , 79 , 81 , 82 , 88 , 90 , 100 ]. Other studies use more complex algorithms such as particle swarm optimization [ 57 ], fuzzy logic [ 68 ], or reinforcement algorithms [ 44 , 84 ].…”

Section: Resultsmentioning

confidence: 99%

“…A total of 4 studies [41,[75][76][77] helped patients to find appropriate doctors for a specific health problem, and 4 other studies [51,[78][79][80] focused on finding nearby hospitals. A total of 2 studies [78,79] simply focused on the clinical preferences of the patients, whereas Krishnan et al [111] "provide health care recommendations that include Blood Donor recommendations and Hospital Specialization." Finally, Tabrizi et al [80] considered patient satisfaction as the primary feature of recommending hospitals to the user.…”

Section: General Health Informationmentioning

confidence: 99%

“…A total of 7 studies [33,34,37,63,79,88,97] or approximately one-fourth of the studies with an interface included visualizations. However, the approach used was different for all studies, as shown in Table 4.…”

Section: Visualizationmentioning

confidence: 99%

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Health Recommender Systems: Systematic Review

Croon¹,

Houdt²,

Htun³

et al. 2021

J Med Internet Res

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Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

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Section: Resultsmentioning

confidence: 99%

Section: General Health Informationmentioning

confidence: 99%

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Health Recommender Systems: Systematic Review

Croon¹,

Houdt²,

Htun³

et al. 2021

J Med Internet Res

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Recommender Systems in Healthcare

Afolabi

Toivanen

2018

Advances in Healthcare Information Systems and Administration

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In this chapter the appropriateness of any recommender system in healthcare, which lies in its ability to provide capabilities for meeting the challenges of modern care giving, is examined. The impacts of over two decades of research in and implementation of recommender systems in healthcare are extensively examined in two consecutive periods: first to examine empirical results and practical implementations while the second focuses on validating the earlier findings and justifying the propositions made. Although the result indicates an optimistic progress and upward trend in both the research and implementation, there are compelling reasons to invest more efforts at harmonizing evaluation criteria and metrics. In addition, in order to appropriately, adequately, and effectively meet the challenges of modern care, the rapidly evolving trends, and changing technologies, a novel solution with potential for these capabilities is proposed: a solution to provide real-time recommendations and make them available for sharing among stakeholders in real time.

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Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems

Afolabi

Toivanen

Haataja

et al. 2018

Intelligent Systems

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This systematic literature review is aimed at examining empirical results and practical implementations of healthcare recommender systems. While fundamentally many of the development of recommender systems in medical and healthcare are based on theory and logic, the performance is always measured in terms of empirical results and practical implementations from evaluation of such systems. Besides, the ultimate judgment of the effectiveness of the methods and algorithms used is often based on the empirical results of recommender systems. Robustness, efficiency, speed, and accuracy are also best determined by empirical results. Extensive search was carried out in some major databases. Literature were grouped into three categories namely core, related, and relevant. The core papers were subjected to further analysis. The result shows that most work reviewed were partially evaluated and have a promising future. Moreover, a yet-to-be explored novel proposal for integration of a recommender system into smart home care is presented.

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Geospatial recommender system for the location of health services

Cited by 4 publications

References 8 publications

Health Recommender Systems: Systematic Review

Health Recommender Systems: Systematic Review

Recommender Systems in Healthcare

Systematic Literature Review on Empirical Results and Practical Implementations of Healthcare Recommender Systems

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