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Yin Z, Kuang Z, Zhang H, Guo Y, Li T, Wu Z, Wang L. Explainable AI Method for Tinnitus Diagnosis via Neighbor-Augmented Knowledge Graph and Traditional Chinese Medicine: Development and Validation Study. JMIR Med Inform 2024; 12:e57678. [PMID: 38857077 PMCID: PMC11196910 DOI: 10.2196/57678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 06/11/2024] Open
Abstract
BACKGROUND Tinnitus diagnosis poses a challenge in otolaryngology owing to an extremely complex pathogenesis, lack of effective objectification methods, and factor-affected diagnosis. There is currently a lack of explainable auxiliary diagnostic tools for tinnitus in clinical practice. OBJECTIVE This study aims to develop a diagnostic model using an explainable artificial intelligence (AI) method to address the issue of low accuracy in tinnitus diagnosis. METHODS In this study, a knowledge graph-based tinnitus diagnostic method was developed by combining clinical medical knowledge with electronic medical records. Electronic medical record data from 1267 patients were integrated with traditional Chinese clinical medical knowledge to construct a tinnitus knowledge graph. Subsequently, weights were introduced, which measured patient similarity in the knowledge graph based on mutual information values. Finally, a collaborative neighbor algorithm was proposed, which scored patient similarity to obtain the recommended diagnosis. We conducted 2 group experiments and 1 case derivation to explore the effectiveness of our models and compared the models with state-of-the-art graph algorithms and other explainable machine learning models. RESULTS The experimental results indicate that the method achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F1-score, and 99% area under the receiver operating characteristic curve for the inference of 5 tinnitus subtypes among 253 test patients. Additionally, it demonstrated good interpretability. The topological structure of knowledge graphs provides transparency that can explain the reasons for the similarity between patients. CONCLUSIONS This method provides doctors with a reliable and explainable diagnostic tool that is expected to improve tinnitus diagnosis accuracy.
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Affiliation(s)
- Ziming Yin
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhongling Kuang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Haopeng Zhang
- Department of Otolaryngology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu Guo
- Department of Otolaryngology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ting Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Zhengkun Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Lihua Wang
- Department of Otolaryngology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Jin S, Liang H, Zhang W, Li H. Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study. JMIR Med Inform 2024; 12:e52210. [PMID: 38409769 PMCID: PMC11004512 DOI: 10.2196/52210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 01/02/2024] [Accepted: 01/06/2024] [Indexed: 02/28/2024] Open
Abstract
Background The incidence of breast cancer has remained high and continues to rise since the 21st century. Consequently, there has been a significant increase in research efforts focused on breast cancer prevention and treatment. Despite the extensive body of literature available on this subject, systematic integration is lacking. To address this issue, knowledge graphs have emerged as a valuable tool. By harnessing their powerful knowledge integration capabilities, knowledge graphs offer a comprehensive and structured approach to understanding breast cancer prevention and treatment. Objective We aim to integrate literature data on breast cancer treatment and prevention, build a knowledge graph, and provide support for clinical decision-making. Methods We used Medical Subject Headings terms to search for clinical trial literature on breast cancer prevention and treatment published on PubMed between 2018 and 2022. We downloaded triplet data from the Semantic MEDLINE Database (SemMedDB) and matched them with the retrieved literature to obtain triplet data for the target articles. We visualized the triplet information using NetworkX for knowledge discovery. Results Within the scope of literature research in the past 5 years, malignant neoplasms appeared most frequently (587/1387, 42.3%). Pharmacotherapy (267/1387, 19.3%) was the primary treatment method, with trastuzumab (209/1805, 11.6%) being the most commonly used therapeutic drug. Through the analysis of the knowledge graph, we have discovered a complex network of relationships between treatment methods, therapeutic drugs, and preventive measures for different types of breast cancer. Conclusions This study constructed a knowledge graph for breast cancer prevention and treatment, which enabled the integration and knowledge discovery of relevant literature in the past 5 years. Researchers can gain insights into treatment methods, drugs, preventive knowledge regarding adverse reactions to treatment, and the associations between different knowledge domains from the graph.
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Affiliation(s)
- Shuyan Jin
- Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
| | - Haobin Liang
- School of Economics and Statistics, Guangzhou University, Guangzhou, China
| | - Wenxia Zhang
- Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
| | - Huan Li
- Health Department, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, China
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Kee G, Kang HJ, Ahn I, Gwon H, Kim Y, Seo H, Choi H, Cho HN, Kim M, Han J, Park S, Kim K, Jun TJ, Kim YH. Are polypharmacy side effects predicted by public data still valid in real-world data? Heliyon 2024; 10:e24620. [PMID: 38304832 PMCID: PMC10831713 DOI: 10.1016/j.heliyon.2024.e24620] [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: 10/24/2023] [Revised: 12/29/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Background and Objective Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.
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Affiliation(s)
- Gaeun Kee
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hee Jun Kang
- Division of Cardiology, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Imjin Ahn
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hansle Gwon
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Yunha Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Hyeram Seo
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Heejung Choi
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Ha Na Cho
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Minkyoung Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - JiYe Han
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Seohyun Park
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Kyuwoong Kim
- National Cancer Control Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, 10408, Goyang, Republic of Korea
| | - Tae Joon Jun
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, 05505, Seoul, Republic of Korea
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Murali L, Gopakumar G, Viswanathan DM, Nedungadi P. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. J Biomed Inform 2023:104403. [PMID: 37230406 DOI: 10.1016/j.jbi.2023.104403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 05/27/2023]
Abstract
With the growth of data and intelligent technologies, the healthcare sector opened numerous technology that enabled services for patients, clinicians, and researchers. One major hurdle in achieving state-of-the-art results in health informatics is domain-specific terminologies and their semantic complexities. A knowledge graph crafted from medical concepts, events, and relationships acts as a medical semantic network to extract new links and hidden patterns from health data sources. Current medical knowledge graph construction studies are limited to generic techniques and opportunities and focus less on exploiting real-world data sources in knowledge graph construction. A knowledge graph constructed from Electronic Health Records (EHR) data obtains real-world data from healthcare records. It ensures better results in subsequent tasks like knowledge extraction and inference, knowledge graph completion, and medical knowledge graph applications such as diagnosis predictions, clinical recommendations, and clinical decision support. This review critically analyses existing works on medical knowledge graphs that used EHR data as the data source at (i) representation level, (ii) extraction level (iii) completion level. In this investigation, we found that EHR-based knowledge graph construction involves challenges such as high complexity and dimensionality of data, lack of knowledge fusion, and dynamic update of the knowledge graph. In addition, the study presents possible ways to tackle the challenges identified. Our findings conclude that future research should focus on knowledge graph integration and knowledge graph completion challenges.
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Affiliation(s)
- Lino Murali
- Center for Research in Analytics and Technologies for Education (CREATE), Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India; Division of Information technology, School of Engineering, Cochin University of Science and Technology, Kochi, 682022, Kerala, India
| | - G Gopakumar
- Department of Computer Science and Engineering, School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India
| | - Daleesha M Viswanathan
- Division of Information technology, School of Engineering, Cochin University of Science and Technology, Kochi, 682022, Kerala, India
| | - Prema Nedungadi
- Center for Research in Analytics and Technologies for Education (CREATE), Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India; Department of Computer Science and Engineering, School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India.
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