It is challenging to characterize the drug-related problems(DRPs)of patients treated by Integrated Traditional Chinese and Western Medicine(ITCWM),both locally and globally.In the present study,we aimed to assess DRPs...It is challenging to characterize the drug-related problems(DRPs)of patients treated by Integrated Traditional Chinese and Western Medicine(ITCWM),both locally and globally.In the present study,we aimed to assess DRPs and factors associated with their occurrence among inpatients of the ITCWM department in China.We retrospectively examined medication use in the ITCWM department,documented in Intelligent Management System Software for Critical Rational Administration of Drug.Various types of DRPs classification were performed based on the Pharmaceutical Care Network Europe(PCNE)classification tool V9.0,and associations of patient’s characteristics were analyzed.A total of 1606 DRPs were identified in 687 inpatients in this study.Problems and causes of DRPs,intervention,acceptance,and outcome of that were classified.PPI(21.7%),endogenous supplements(15.4%),and traditional Chinese medicine(8.7%)contributed to the significant proportion of drug categories associated with DRPs.Approximately half of the patients(47.6%)had at least one DRP.The top four categories of causes were“drug administered via the wrong route”(18.4%),“inappropriate combination”(16.7%),“too long duration of the treatment”(13.0%),and“inappropriate drug form”(12.1%).Total 63.6%of intervention was accepted and fully implemented.“EPP”and“hospital stay days≥10 d”characteristics were most likely to be significantly associated with DRPs.As a necessary review item,DRP was highly performed among inpatients of the ITCWM department.The work provided a benchmark for this population through the PCNE strategy.展开更多
Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages suc...Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.展开更多
基金National Major New Drug Creation Project of China(Grant No.2020ZX09201-009)。
文摘It is challenging to characterize the drug-related problems(DRPs)of patients treated by Integrated Traditional Chinese and Western Medicine(ITCWM),both locally and globally.In the present study,we aimed to assess DRPs and factors associated with their occurrence among inpatients of the ITCWM department in China.We retrospectively examined medication use in the ITCWM department,documented in Intelligent Management System Software for Critical Rational Administration of Drug.Various types of DRPs classification were performed based on the Pharmaceutical Care Network Europe(PCNE)classification tool V9.0,and associations of patient’s characteristics were analyzed.A total of 1606 DRPs were identified in 687 inpatients in this study.Problems and causes of DRPs,intervention,acceptance,and outcome of that were classified.PPI(21.7%),endogenous supplements(15.4%),and traditional Chinese medicine(8.7%)contributed to the significant proportion of drug categories associated with DRPs.Approximately half of the patients(47.6%)had at least one DRP.The top four categories of causes were“drug administered via the wrong route”(18.4%),“inappropriate combination”(16.7%),“too long duration of the treatment”(13.0%),and“inappropriate drug form”(12.1%).Total 63.6%of intervention was accepted and fully implemented.“EPP”and“hospital stay days≥10 d”characteristics were most likely to be significantly associated with DRPs.As a necessary review item,DRP was highly performed among inpatients of the ITCWM department.The work provided a benchmark for this population through the PCNE strategy.
基金supported by the NSFC (Grant Nos. 61772281,61703212, 61602254)Jiangsu Province Natural Science Foundation [grant numberBK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cationInstitutions (PAPD) and Jiangsu Collaborative Innovation Center on AtmosphericEnvironment and Equipment Technology (CICAEET).
文摘Multi-label text categorization refers to the problem of categorizing text througha multi-label learning algorithm. Text classification for Asian languages such as Chinese isdifferent from work for other languages such as English which use spaces to separate words.Before classifying text, it is necessary to perform a word segmentation operation to converta continuous language into a list of separate words and then convert it into a vector of acertain dimension. Generally, multi-label learning algorithms can be divided into twocategories, problem transformation methods and adapted algorithms. This work will usecustomer's comments about some hotels as a training data set, which contains labels for allaspects of the hotel evaluation, aiming to analyze and compare the performance of variousmulti-label learning algorithms on Chinese text classification. The experiment involves threebasic methods of problem transformation methods: Support Vector Machine, Random Forest,k-Nearest-Neighbor;and one adapted algorithm of Convolutional Neural Network. Theexperimental results show that the Support Vector Machine has better performance.
