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Wen P, Zhao Y, Liu J. A systematic knowledge graph-based smart management method for operations: A case study of standardized management. Heliyon 2023; 9:e20390. [PMID: 37780784 PMCID: PMC10539963 DOI: 10.1016/j.heliyon.2023.e20390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 10/03/2023] Open
Abstract
Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on.
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Affiliation(s)
- Peihan Wen
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, PR China
| | - Yiming Zhao
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, PR China
| | - Jin Liu
- School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, PR China
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Abu-Salih B, AL-Qurishi M, Alweshah M, AL-Smadi M, Alfayez R, Saadeh H. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. JOURNAL OF BIG DATA 2023; 10:81. [PMID: 37274445 PMCID: PMC10225120 DOI: 10.1186/s40537-023-00774-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 05/17/2023] [Indexed: 06/06/2023]
Abstract
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
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Affiliation(s)
| | | | | | - Mohammad AL-Smadi
- Jordan University of Science and Technology, Irbid, Jordan
- Qatar University, Doha, Qatar
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Torab-Miandoab A, Poursheikh Asghari M, Hashemzadeh N, Ferdousi R. Analysis and identification of drug similarity through drug side effects and indications data. BMC Med Inform Decis Mak 2023; 23:35. [PMID: 36788528 PMCID: PMC9926629 DOI: 10.1186/s12911-023-02133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects. METHODS The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity. RESULTS Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities. CONCLUSION Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.
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Affiliation(s)
- Amir Torab-Miandoab
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Mehdi Poursheikh Asghari
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Nastaran Hashemzadeh
- grid.412888.f0000 0001 2174 8913Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran ,grid.412888.f0000 0001 2174 8913Research Center for Pharmaceutical Nanotechnology, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Ferdousi
- Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711, Iran.
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Hotness prediction of scientific topics based on a bibliographic knowledge graph. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhu X, Gu Y, Xiao Z. HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning. Front Genet 2022; 13:799349. [PMID: 35571049 PMCID: PMC9091197 DOI: 10.3389/fgene.2022.799349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.
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Affiliation(s)
- Xian Zhu
- School of Information Management, Nanjing University, Nanjing, China
- School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yueming Gu
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, University of Melbourne, Parkville, VIC, Australia
| | - Zhifeng Xiao
- School of Engineering, Penn State Erie, The Behrend College, Erie, PA, United States
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Weng H, Chen J, Ou A, Lao Y. Leveraging Representation Learning for the Construction and Application of Knowledge Graph for Traditional Chinese Medicine (Preprint). JMIR Med Inform 2022; 10:e38414. [PMID: 36053574 PMCID: PMC9482071 DOI: 10.2196/38414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/04/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Heng Weng
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jielong Chen
- School of Information Science, Guangdong University of Finance & Economics, Guangzhou, China
| | - Aihua Ou
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yingrong Lao
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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Shang Y, Tian Y, Zhou M, Zhou T, Lyu K, Wang Z, Xin R, Liang T, Zhu S, Li J. EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice. IEEE J Biomed Health Inform 2021; 25:2463-2475. [PMID: 34057901 DOI: 10.1109/jbhi.2021.3085003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71,679 patients in non-nephrology departments. The system identified 2,774 patients meeting CKD diagnosis criteria and 10,377 patients requiring high attention. A follow-up study of 5,439 patients showed that 82.1% of patients who met the diagnosis criteria and 61.4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.
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Ren Y, Shi Y, Zhang K, Chen Z, Yan Z. Medical Treatment Migration Prediction Based on GCN via Medical Insurance Data. IEEE J Biomed Health Inform 2020; 24:2516-2522. [PMID: 32750955 DOI: 10.1109/jbhi.2020.3008493] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nowadays, prediction for medical treatment migration has become one of the interesting issues in the field of health informatics. This is because the medical treatment migration behavior is closely related to the evaluation of regional medical level, the rational use of medical resources, and the distribution of medical insurance. Therefore, a prediction model for medical treatment migration based on medical insurance data is introduced in this paper. First, a medical treatment graph is constructed based on medical insurance data. The medical treatment graph is a heterogeneous graph, which contains entities such as patients, diseases, hospitals, medicines, hospitalization events, and the relations between these entities. However, existing graph neural networks are unable to capture the time-series relationships between event-type entities. To this end, a prediction model based on Graph Convolutional Network (GCN) is proposed in this paper, namely, Event-involved GCN (EGCN). The proposed model aggregates conventional entities based on attention mechanism, and aggregates event-type entities based on a gating mechanism similar to LSTM. In addition, jumping connection is deployed to obtain the final node representation. In order to obtain embedded representations of medicines based on external information (medicine descriptions), an automatic encoder capable of embedding medicine descriptions is deployed in the proposed model. Finally, extensive experiments are conducted on a real medical insurance data set. Experimental results show that our model's predictive ability is better than the best models available.
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Abstract
Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA
| | - Ignacio J Tripodi
- Department of Computer Science, University of Colorado, Boulder, Colorado 80309, USA
| | - Harrison Pielke-Lombardo
- Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA
| | - Lawrence E Hunter
- Computational Bioscience Program and Department of Pharmacology, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado 80045, USA
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Li L, Wang P, Wang Y, Wang S, Yan J, Jiang J, Tang B, Wang C, Liu Y. A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development. JMIR Med Inform 2020; 8:e17645. [PMID: 32436854 PMCID: PMC7273238 DOI: 10.2196/17645] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 11/26/2022] Open
Abstract
Background Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. Objective We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. Methods We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. Results Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. Conclusions The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.
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Affiliation(s)
- Linfeng Li
- Institute of Information Science, Beijing Jiaotong University, Beijing, China.,Yidu Cloud Technology Inc, Beijing, China
| | - Peng Wang
- College of Computer Science, Chongqing University, Chongqing, China.,Southwest Hospital, Chongqing, China
| | - Yao Wang
- Yidu Cloud Technology Inc, Beijing, China
| | - Shenghui Wang
- Institute of Information Science, Beijing Jiaotong University, Beijing, China
| | - Jun Yan
- Yidu Cloud Technology Inc, Beijing, China
| | | | - Buzhou Tang
- Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Chengliang Wang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yuting Liu
- School of Science, Beijing Jiaotong University, Beijing, China
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