Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer
Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8156 |
| _version_ | 1797411334293291008 |
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| author | Jae-Kwon Kim Sun-Jung Lee Sung-Hoo Hong In-Young Choi |
| author_facet | Jae-Kwon Kim Sun-Jung Lee Sung-Hoo Hong In-Young Choi |
| author_sort | Jae-Kwon Kim |
| collection | DOAJ |
| description | Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems. |
| first_indexed | 2024-03-09T04:43:36Z |
| format | Article |
| id | doaj.art-0e934fe9b9a644078dfc250bce858868 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:43:36Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0e934fe9b9a644078dfc250bce8588682023-12-03T13:17:33ZengMDPI AGApplied Sciences2076-34172022-08-011216815610.3390/app12168156Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate CancerJae-Kwon Kim0Sun-Jung Lee1Sung-Hoo Hong2In-Young Choi3Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaDepartment of Urology, Seoul St. Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul 06591, KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, KoreaClinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems.https://www.mdpi.com/2076-3417/12/16/8156digital twinmachine learningprostate cancerpathology stagebiochemical recurrence |
| spellingShingle | Jae-Kwon Kim Sun-Jung Lee Sung-Hoo Hong In-Young Choi Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer Applied Sciences digital twin machine learning prostate cancer pathology stage biochemical recurrence |
| title | Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer |
| title_full | Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer |
| title_fullStr | Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer |
| title_full_unstemmed | Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer |
| title_short | Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer |
| title_sort | machine learning based digital twin system for predicting the progression of prostate cancer |
| topic | digital twin machine learning prostate cancer pathology stage biochemical recurrence |
| url | https://www.mdpi.com/2076-3417/12/16/8156 |
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