Finding Right Doctors and Hospitals: A Personalized Health Recommender

Sridevi, M.; Rao, R. Rajeshwara

doi:10.1007/978-981-13-0586-3_69

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Fifty-eight percent of the included studies (37/63) disclosed the names of their HRSs and, 19 of them were web-based systems [ 33 , 36 , 43 , 54 , 56 , 57 , 58 , 59 , 60 , 68 , 69 , 70 , 73 , 75 , 76 , 78 , 80 , 81 , 83 ], and 17 were mobile app systems [ 23 , 25 , 28 , 30 , 31 , 40 , 41 , 42 , 44 , 45 , 46 , 51 , 63 , 64 , 74 , 79 , 85 ].…”

Section: Resultsmentioning

confidence: 99%

“…A few studies even used both types of methods. (1) System evaluation involving users: while there were 22 studies that recruited the end users of the systems and tested the feasibility of the HRSs [ 25 , 27 , 28 , 32 , 33 , 41 , 42 , 43 , 44 , 55 , 57 , 59 , 63 , 64 , 68 , 69 , 70 , 76 , 78 , 80 , 81 , 82 ], fourteen studies measured the performance of HRS through comparing the accuracy of different groups [ 26 , 29 , 31 , 36 , 42 , 45 , 48 , 49 , 53 , 60 , 61 , 66 , 67 , 74 ]. For example, an HRS was compared with health conditions in general or no recommendation scenarios.…”

Section: Resultsmentioning

confidence: 99%

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Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Cai

Kumar

et al. 2022

IJERPH

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A health recommender system (HRS) provides a user with personalized medical information based on the user’s health profile. This scoping review aims to identify and summarize the HRS development in the most recent decade by focusing on five key aspects: health domain, user, recommended item, recommendation technology, and system evaluation. We searched PubMed, ACM Digital Library, IEEE Xplore, Web of Science, and Scopus databases for English literature published between 2010 and 2022. Our study selection and data extraction followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. The following are the primary results: sixty-three studies met the eligibility criteria and were included in the data analysis. These studies involved twenty-four health domains, with both patients and the general public as target users and ten major recommended items. The most adopted algorithm of recommendation technologies was the knowledge-based approach. In addition, fifty-nine studies reported system evaluations, in which two types of evaluation methods and three categories of metrics were applied. However, despite existing research progress on HRSs, the health domains, recommended items, and sample size of system evaluation have been limited. In the future, HRS research shall focus on dynamic user modelling, utilizing open-source knowledge bases, and evaluating the efficacy of HRSs using a large sample size. In conclusion, this study summarized the research activities and evidence pertinent to HRSs in the most recent ten years and identified gaps in the existing research landscape. Further work shall address the gaps and continue improving the performance of HRSs to empower users in terms of healthcare decision making and self-management.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Cai

Kumar

et al. 2022

IJERPH

View full text Add to dashboard Cite

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“…This approach successfully generates doctor recommendations for patients. Sridevi and Rajeshwara Rao [13] proposed a personalized health recommender system that recommends hospitals and doctors to patients. The proposed system uses a CF-based recommendation approach that exploits patients' demographic data, the similarity between patients based on their ratings, the total number of reviews, and average ratings to generate a personalized list of top-rated hospitals or doctors for patients.…”

Section: Related Workmentioning

confidence: 99%

An Effective Doctor Recommendation Algorithm for Online Healthcare Platforms

SHAMBOUR,

ABUALHAJ,

SHAREHA

2024

ROMJIST

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The emergence of online healthcare platforms provides patients with convenience, but choosing the right doctor among the thousands of doctors available on these platforms has become a challenge for patients. The majority of these platforms recommend the same doctors to all patients based on a global ranking, disregarding individual patient preferences. The use of recommender systems helps to resolve this issue by assisting patients in locating doctors who meet their preferences and requirements. Particularly, Collaborative Filtering (CF) algorithms have been extensively utilized to generate personalized recommendations for a variety of applications. Despite their success, they still need to be further optimized to address both the sparsity and cold-start problems due to insufficient data. In this paper, we propose an effective doctor recommendation approach to assist patients in searching for satisfactory doctors who precisely match their preferences regardless of time and location. The proposed approach employs Multi-Criteria CF and content filtering to enhance the quality of recommendations by mitigating the impact of data sparsity and cold start challenges. Offline tests conducted on a real-world dataset show that the proposed approach is superior to state-of-the-art approaches in addressing the aforementioned issues and boosting prediction accuracy and coverage.

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“…Many doctor recommendation methods (Pan et al , 2019; Sridevi and Rajeshwara Rao, 2019; Xu et al , 2019; Ye et al , 2019) have been proposed given the above necessity. However, previous methods seldom consider the data sparsity problem in the context of online healthcare consultation platforms.…”

Section: Introductionmentioning

confidence: 99%

Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

Yuan

Deng

2021

INTR

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PurposeRecommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.Design/methodology/approachThis research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.FindingsThe proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.Originality/valueThis study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

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Finding Right Doctors and Hospitals: A Personalized Health Recommender

Cited by 5 publications

References 11 publications

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review

An Effective Doctor Recommendation Algorithm for Online Healthcare Platforms

Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

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