2022
DOI: 10.1080/07853890.2022.2160008
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Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

Abstract: Background and aims Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. Methods 4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, wh… Show more

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Cited by 15 publications
(7 citation statements)
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“…Traditional machine learning and deep learning constitute artificial intelligence, which is currently widely used in the medical field [14,15]. Zhou et al [16] conducted survival analysis on intrahepatic cholangiocarcinoma using multicenter data based on machine learning modeling and prediction and achieved good results. In 2018, Katzman et al [17] proposed DeepSurv, which is a survival prediction method based on neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…Traditional machine learning and deep learning constitute artificial intelligence, which is currently widely used in the medical field [14,15]. Zhou et al [16] conducted survival analysis on intrahepatic cholangiocarcinoma using multicenter data based on machine learning modeling and prediction and achieved good results. In 2018, Katzman et al [17] proposed DeepSurv, which is a survival prediction method based on neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…Zhou et al. ( 93 ) established three RF models and developed an online tool, with an AUC of 0.7478. Serum albumin-to-fibrinogen ratio and CA 19-9 were used to develop the DeepSurv model to predict the prognosis of ECC and guide individualized postoperative chemotherapy as shown in the study by Wang et al.…”
Section: Ai and Prognosismentioning
confidence: 99%
“…However, we acknowledge the technical difficulty, the considerable cost, and the potential ethical issues of carrying out such meta-analyses in cancer survivors. The implementation of novel research methods, such as the analysis of big data contained in population-based studies and registries with machine learning 132 , 133 , might serve as reliable alternatives for investigating the role of MBS in preventing gynecologic cancer recurrence and extending survivorship.…”
Section: Closing Remarksmentioning
confidence: 99%