Clinical decision support system for hypertension medication based on knowledge graph

Zhou, Gengxian; Haihong, E; Kuang, Zemin; Tan, Ling; Xie, Xiaoxuan; Li, Jundi; Luo, Haoran

doi:10.1016/j.cmpb.2022.107220

Cited by 21 publications

(11 citation statements)

References 9 publications

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“…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%

“…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%

“…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%

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

Zhang¹,

Yi²,

Sun³

et al. 2023

Interdisciplinary Nursing Research

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Kgrs Evaluationmentioning

confidence: 99%

Section: Kgrs Evaluationmentioning

confidence: 99%

Section: Disease-assisted Decision Makingmentioning

confidence: 99%

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

Zhang¹,

Yi²,

Sun³

et al. 2023

Interdisciplinary Nursing Research

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“…CDSS for hypertension medication based on a knowledge graph, developed in [14], helps in the implementation of the convenient and intuitive hypertension medication and knowledge management by applying knowledge from the knowledge graph.…”

Section: State-of-the-artmentioning

confidence: 99%

Methodology for the development and application of clinical decisions support information technologies with consideration of civil-legal grounds

Hnatchuk

Hovorushchenko²,

Sergeenko³

2023

reks

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Currently, there are no clinical decision support information technologies (CDSIT) that would consider civil-legal grounds when forming a decision for clinicians. Therefore, the design, development, and implementation of CDSIT, which considers civil-legal grounds when forming decisions, are actual problems. Methodology for the development and application of knowledge-driven, rule-based, clinical decisions support information technologies with consideration of civil-legal grounds has been developed, which provides a theoretical basis for developing clinical decisions support information technology with consideration of civil-legal grounds and partial CDSITs regarding the possibility of providing medical services of a certain type. In addition to the conclusion about the possibility or impossibility of providing certain medical services, the developed methodology ensures the presence of all essential terms (from the viewpoint of civil law regulation) in the contract for the certain medical service's provision and/or the data on potential patients for the provision of such a service, as well as minimization of the influence of the human factor when making clinical decisions. It is advisable to evaluate the CDSITs with consideration of civil-legal grounds, developed according to the proposed methodology, from the viewpoint of the correctness of the decisions generated by them, as well as from the viewpoint of their usefulness for clinics. In this paper, experiments with the methodology-based CDSIT regarding the possibility of performing a surrogate motherhood procedure with consideration of civil-legal grounds were conducted. Such experiments showed the correctness of the generated decisions at the level of 97 %. Experiments also demonstrated the usefulness of such IT for clinics from the viewpoint of eliminating adverse legal consequences, as they might arise due to violation or disregard of legal, and moral and ethical norms.

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DAAR: Drift Adaption and Alternatives Ranking approach for interpretable clinical decision support systems

Kavya

Kala

Christopher

et al. 2023

Biomedical Signal Processing and Control

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Clinical decision support system for hypertension medication based on knowledge graph

Cited by 21 publications

References 9 publications

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

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

Methodology for the development and application of clinical decisions support information technologies with consideration of civil-legal grounds

DAAR: Drift Adaption and Alternatives Ranking approach for interpretable clinical decision support systems

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