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Wang X, Lin Y, Xiong Y, Zhang S, He Y, He Y, Zhang Z, Plasek JM, Zhou L, Bates DW, Tang C. Using an optimized generative model to infer the progression of complications in type 2 diabetes patients. BMC Med Inform Decis Mak 2022; 22:174. [PMID: 35778708 PMCID: PMC9250218 DOI: 10.1186/s12911-022-01915-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/22/2022] [Indexed: 02/08/2023] Open
Abstract
Background People live a long time in pre-diabetes/early diabetes without a formal diagnosis or management. Heterogeneity of progression coupled with deficiencies in electronic health records related to incomplete data, discrete events, and irregular event intervals make identification of pre-diabetes and critical points of diabetes progression challenging. Methods We utilized longitudinal electronic health records of 9298 patients with type 2 diabetes or prediabetes from 2005 to 2016 from a large regional healthcare delivery network in China. We optimized a generative Markov-Bayesian-based model to generate 5000 synthetic illness trajectories. The synthetic data were manually reviewed by endocrinologists. Results We build an optimized generative progression model for type 2 diabetes using anchor information to reduce the number of parameters learning in the third layer of the model from \documentclass[12pt]{minimal}
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\begin{document}$$O\left(N\times W\right)$$\end{document}ON×W to \documentclass[12pt]{minimal}
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\begin{document}$$O\left((N-C)\times W\right)$$\end{document}O(N-C)×W, where \documentclass[12pt]{minimal}
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\begin{document}$$N$$\end{document}N is the number of clinical findings, \documentclass[12pt]{minimal}
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\begin{document}$$W$$\end{document}W is the number of complications, \documentclass[12pt]{minimal}
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\begin{document}$$C$$\end{document}C is the number of anchors. Based on this model, we infer the relationships between progression stages, the onset of complication categories, and the associated diagnoses during the whole progression of type 2 diabetes using electronic health records. Discussion Our findings indicate that 55.3% of single complications and 31.8% of complication patterns could be predicted early and managed appropriately to potentially delay (as it is a progressive disease) or prevented (by lifestyle modifications that keep patient from developing/triggering diabetes in the first place). Conclusions The full type 2 diabetes patient trajectories generated by the chronic disease progression model can counter a lack of real-world evidence of desired longitudinal timeframe while facilitating population health management.
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Affiliation(s)
- Xiaoxia Wang
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, 200438, China.,College of Computer Science and Engineering, Northwest Normal University, Gansu, 730070, China
| | - Yifei Lin
- West China Hospital of Sichuan University, Sichuan, 610041, China
| | - Yun Xiong
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, 200438, China
| | - Suhua Zhang
- Department of Kidney Disease, Suzhou Kowloon Hospital, Shanghai Jiao Tong University School of Medicine, Jiangsu, 215021, China
| | - Yanming He
- Department of Endocrinology, Yueyang Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200437, China
| | - Yuqing He
- Institute for Data Industry, School of Economics, Fudan University, Shanghai, 200433, China
| | - Zhikun Zhang
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, 200438, China.,Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph M Plasek
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Li Zhou
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.,Clinical and Quality Analysis, Mass General Brigham, Boston, MA, 02145, USA
| | - Chunlei Tang
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. .,Clinical and Quality Analysis, Mass General Brigham, Boston, MA, 02145, USA. .,Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, 1620 Tremont Street, Boston, MA, 02120, USA.
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Alizadehsani R, Roshanzamir M, Hussain S, Khosravi A, Koohestani A, Zangooei MH, Abdar M, Beykikhoshk A, Shoeibi A, Zare A, Panahiazar M, Nahavandi S, Srinivasan D, Atiya AF, Acharya UR. Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020). ANNALS OF OPERATIONS RESEARCH 2021; 339:1-42. [PMID: 33776178 PMCID: PMC7982279 DOI: 10.1007/s10479-021-04006-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/23/2021] [Indexed: 05/17/2023]
Abstract
Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189 Fasa, Iran
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Dibrugarh, Assam 786004 India
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Afsaneh Koohestani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | | | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Adham Beykikhoshk
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California, San Francisco, USA
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Dipti Srinivasan
- Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo, 12613 Egypt
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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Sambo F, Facchinetti A, Hakaste L, Kravic J, Di Camillo B, Fico G, Tuomilehto J, Groop L, Gabriel R, Tiinamaija T, Cobelli C. A Bayesian Network for Probabilistic Reasoning and Imputation of Missing Risk Factors in Type 2 Diabetes. Artif Intell Med 2015. [DOI: 10.1007/978-3-319-19551-3_22] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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