Deep learning Based Patient-Friendly Clinical Expert Recommendation Framework

Kumar, Akhilesh; Khan, Sarfraz Fayaz; Sodhi, Rajinder; Khan, Ihtiram Raza; Kumar, Sumit; Tamrakar, Ashish Kumar

doi:10.1109/iciptm54933.2022.9754157

Cited by 2 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, a total of 1035 articles were excluded after the title and abstract screening, and 200 articles were excluded after the full-text screening. Finally, 16 studies were included in this scoping review [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] . We also utilized a similar process to retrieve 222 grants from the NSFC official and relevant websites, and 4 records identified from other sources.…”

Section: Resultsmentioning

confidence: 99%

“…In this review, 8 included studies used the KGRS in disease decision support 12,14,15,19,[22][23][24]27 . The KGRS was mainly used to provide a good environment and technical basis for sharing and disseminating medical knowledge, standardizing treatments, and providing scientific advices for clinical decision making.…”

Section: Disease-assisted Decision Makingmentioning

confidence: 99%

“…They also linked user reviews and medical service quality, and used the deep learning model to give the doctors' service quality evaluation model, which provided users with a more open and reasonable recommendation service. Similarly, Kumar et al 15 constructed a patientfriendly clinical expert recommendation system based on knowledge graphs and deep learning algorithms 15 .…”

Section: Disease-assisted Decision Makingmentioning

confidence: 99%

“…Among the 16 included studies, 13 studies evaluated KGRS using performance metrics, such as accuracy, recall, F1 scores, and method of area under the curve (AUC). The results all showed good model performance 12,[14][15][16][17][18][19][20][22][23][24][25]27 . In addition, 7 studies utilized questionnaires, case, follow-up, or pilot studies based on performance metrics to provide a comprehensive and systematic assessment of KGRS performance from the user's perspectives 12,14,15,17,18,21,22 .…”

Section: Kgrs Evaluationmentioning

confidence: 99%

“…The results all showed good model performance 12,[14][15][16][17][18][19][20][22][23][24][25]27 . In addition, 7 studies utilized questionnaires, case, follow-up, or pilot studies based on performance metrics to provide a comprehensive and systematic assessment of KGRS performance from the user's perspectives 12,14,15,17,18,21,22 . For example, Shang et al 14 evaluated the performance of the KGRS as an aid for chronic kidney disease diagnosis in a follow-up study.…”

Section: Kgrs Evaluationmentioning

confidence: 99%

See 4 more Smart Citations

The application of health recommender systems based on knowledge graph: a scoping review

Zhang¹,

Yi²,

Sun³

et al. 2023

Interdisciplinary Nursing Research

View full text Add to dashboard Cite

Background: Tailored knowledge graph-based recommender systems (KGRSs) have been demonstrated to be able to provide accurate and effective health recommendations to users, and thus significantly reduce health care costs. They are now strongly recommended to be applied in the health care field. Objective: This scoping review aims to identify the current application of KGRSs, their target users and performance metrics, and the potential limitations of implementing health recommender systems in clinical practice. Methods: A review of the studies published from inception to November 1, 2022 was conducted, using key search terms in 6 scientific databases to identify health recommender systems based on knowledge graph technology. Key information from the included studies was extracted and charted. The scoping review was reported following the PRISMA Extension for Scoping Reviews. Result: We included 16 studies and 5 grants totally about the health recommender systems based on knowledge graph technology. They were used in different health areas: traditional Chinese medicine, health management, disease-related decision support, diet, and nutrition recommendations. Among them, 6 studies were for the general public and 6 were for physicians. A total of 13 (81.25%) studies evaluated the KGRS using performance metrics, such as accuracy, recall, F1 score, and area under the curve. All studies pointed out the limitations of the recommender systems and provided directions for their subsequent optimization and improvement. Conclusion: This review describes the state-of-the-art and potential limitations of KGRS used in the health care field. This novel approach has been proven to be effective in overcoming the drawbacks of traditional algorithms, helping users filter massive amounts of data to find out the personalized information they need. Its great potential in digital health needs to be further explored.

show abstract