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Using intelligent Screening Service Platform(iSSP)to improve the Screening Process of clinical Trial Subjects during COViD-19 Pandemic:An Experimental Study

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Data intelligence 2024年第3期6卷 666-691页

作者： Bin Li Runfang Guo Huan Zhou Yuanyuan Liu Xiaolei Zhang Qian Zhang Drug clinical Trial institution The First Affiliated Hospital of Bengbu Medical CollegeBengbu233004China School of Public Health Bengbu Medical CollegeBengbu233030China

Background:During the COViD-19 pandemic,clinical trial recruitment could not be carried out due to travel restrictions,transmission risks and other factors,resulting in the stagnation of many ongoing or upcoming clinical ***:An intelligent screening tool was developed using artificial intelligence technology to rapidly prescreen potential patients for phase i solid tumor drug clinical ***:A total of 429 screening process records were collected from 27 phase i solid tumor drug clinical trials at the First Affiliated Hospital of Bengbu Medical College from April 2018 to May *** of the experimental data were analyzed,and the collinearity(principal component analysis)and strong correlation(χ^(2)test)among features were ***,random forest,and naive Bayes were used to determine the weight importance of the ***,prescreening models were constructed using a classification machine learning algorithm,and the optimal model was ***:Among the 429 screening records,33 were generated by repeated subject participation in different clinical trials,and of the remaining 396 screening records,246(62.12%)were screened *** gold standard for subject screening success was the final judgment made by the principal investigator(Pi)based on the clinical trial protocol.A Venn diagram was used to identify the important feature intersections of the machine learning *** intersecting the top 15 characteristic variables of the different feature screening models,9 common variables were obtained:age,sex,distance from residence to the central institution,tumor histology,tumor stage,tumorectomy,interval from diagnosis/postoperative to screening,chemotherapy,and Eastern Cooperative Oncology Group(ECOG)*** select the optimal subset,the 9 important feature variables were expanded to 12 and 15 feature subsets,and the performance of different feature subsets under different machine learning models was vali

关键词： phase i clinical trials Solid tumors Patient screening COViD-19 Artificial intelligence Machine learning APP

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Stepwise Method Based on Confidence Bound and information incorporation for identifying the Maximum Tolerable Dose

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Northeastern Mathematical Journal 2005年第1期21卷 117-126页

作者：王雪丽陶剑史宁中 Department of Mathematics Daqing Petroleum institute Daqing Heilongjiang163318 Research Instetute of Mathematics Jilin University Changchun130012 Department of Mathematics Northeast Normal University Changchun 130024

The primary goal of a phase i clinical trial is to find the maximum tolerable dose of a treatment. in this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.

关键词： confidence bound continual reassessment method information incorporation maximum tolerable dose phase i clinical trials stepwise method

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