دورية أكاديمية
Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study
| العنوان: | Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study |
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| المؤلفون: | Kyung Hwa Lee, Gwang Hyeon Choi, Jihye Yun, Jonggi Choi, Myung Ji Goh, Dong Hyun Sinn, Young Joo Jin, Minseok Albert Kim, Su Jong Yu, Sangmi Jang, Soon Kyu Lee, Jeong Won Jang, Jae Seung Lee, Do Young Kim, Young Youn Cho, Hyung Joon Kim, Sehwa Kim, Ji Hoon Kim, Namkug Kim, Kang Mo Kim |
| المصدر: | npj Digital Medicine, Vol 7, Iss 1, Pp 1-8 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Abstract The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell’s C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2398-6352 82526885 |
| العلاقة: | https://doaj.org/toc/2398-6352Test |
| DOI: | 10.1038/s41746-023-00976-8 |
| الوصول الحر: | https://doaj.org/article/db78a9799bd04ddca825268850366cd4Test |
| رقم الانضمام: | edsdoj.b78a9799bd04ddca825268850366cd4 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 23986352 82526885 |
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| DOI: | 10.1038/s41746-023-00976-8 |