[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124063-en":3,"doc-seo-124063-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124063,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-based nonlinear regression-adjusted real-time quality control modeling - a multi-center study","Objectives: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring testing performance, faces uncertainty in generalizability across analytes, instruments, laboratories, and hospitals. This work builds a machine learning, nonlinear regression-adjusted, patient-based real-time quality control framework (mNL-PBRTQC) for broader application. Methods: Computer simulation introduced artificial biases into patient data for 10 measurands, training on eight hospital databases and validating on three independent hospital datasets. Results: mNL-PBRTQC outperformed IFCC PBRTQC and linear-regression adjusted RARTQC across all measurands and biases.","Yu-fang Liang, Andrea Padoan, Zhe Wang, Chao Chen, Qing-tao Wang*, Mario Plebani* and Rui Zhou*  \nMachine learning-based nonlinear regressionadjusted real-time quality control modeling: a multi-center study  \n[https://doi.org/10.1515/cclm-2023-0964](https://doi.org/10.1515/cclm-2023-0964)  \nReceived August 31, 2023; accepted October 25, 2023; published online November 21, 2023  \nAbstract  \nObjectives: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in realworld settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application.  \nMethods: Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals’ independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model. Results: Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using  \nYu-fang Liang, Andrea Padoan and Zhe Wang contributed equally to this work and should be considered first authors.  \n*Corresponding authors: Qing-tao Wang and Rui Zhou, Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China, and Beijing Center for Clinical Laboratories, No. 8 Gongti South Road, Chaoyang District, Beijing, 100020, P.R. China, [E-mail: wqt36@163.com](E-mail: wqt36@163.com) (Q.-t. Wang), [zr-molly@163.com](zr-molly@163.com) (R. Zhou); and Mario Plebani, Department of Medicine-DIMED, University of Padova, Padova, Italy, Phone: +39049663240, Fax: +39049663240, [E-mail: mario.plebani@unipd.it](E-mail: mario.plebani@unipd.it). [https://orcid.org/0000-0002-0270-](https://orcid.org/0000-0002-0270-)  \n1711 (M. Plebani)  \nYu-fang Liang, Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China  \nAndrea Padoan, Laboratory Medicine Unit, University-Hospital of Padova, Padova, Italy. [https://orcid.org/0000-0003-1284-7885](https://orcid.org/0000-0003-1284-7885)  \nZhe Wang and Chao Chen, Beijing Jinfeng Yitong Technology Co., Ltd, Beijing, P.R. China  \nplatelets as an example, it was found that for 20 % bias, both positive and negative, the uncertainty of error detection formNL-PBRTQC was smallest at the median and maximum values.  \nConclusions: mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases.  \nKeywords: patient-based real-time quality control; machine learning; nonlinear regression; residual  \nIntroduction  \nSince the work of Bull [1] based on the “average of normals”concept described by Hoffman in 1965 (ref), the idea of patient-based real-time quality control (PBRTQC) has been routinely used as a supportive quality assurance tool in hematology. However, implementing PBRTQC into clinical chemistry and immunoassay has been slow. Now, PBRTQCis gaining increasing attention thanks to sophisticated statistical methodologies, improved information technology capabilities and increasing awareness of the limitations of traditional quality control programs (TQC) [2–8] PBRTQC involves using statistical manipulations of patient results produced from routine clinical analysis [9, 10] . PBRTQC algorithms include already established procedures, moving median (MM), moving average (MA) and exponentially weighted MA (EWMA) [1, 11–16] . Later, the moving standard deviat","cbCainTRCw3mHskW","https://ap.wps.com/l/cbCainTRCw3mHskW","pdf",3982347,1,11,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction\n## Background of PBRTQC\n## Limitations of conventional QC\n# Materials and methods\n## Data collection","[{\"question\":\"What problem does mNL-PBRTQC address compared with conventional QC?\",\"answer\":\"PBRTQC is designed to monitor testing performance using patient results, reducing dependence on commercial QC materials and improving sensitivity for true errors and specificity for false rejection. The proposed mNL-PBRTQC further targets limitations in generalizability across different settings by using nonlinear regression–adjusted machine learning.\"},{\"question\":\"How was the mNL-PBRTQC model built and validated?\",\"answer\":\"Artificial biases were added via computer simulation to patient data for 10 measurands. The model was trained using eight hospital laboratory databases and validated using independent patient datasets from three other hospitals.\"},{\"question\":\"What were the main findings from the independent test datasets?\",\"answer\":\"In all three independent datasets, mNL-PBRTQC outperformed the IFCC PBRTQC approach and linear regression-adjusted real-time quality control for all measurands and all applied biases, with specific behavior such as smaller uncertainty around the median for 20% bias in the platelet example.\"}]","Machine learning-based nonlinear regression-adjusted real-time quality control modeling - a multi-center study | PDF",1785820150,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-nonlinear-regression-adjusted-real-time-quality-control-modeling-a-multi-center-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-nonlinear-regression-adjusted-real-time-quality-control-modeling-a-multi-center-study/124063/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does mNL-PBRTQC address compared with conventional QC?","Question",{"text":75,"@type":76},"PBRTQC is designed to monitor testing performance using patient results, reducing dependence on commercial QC materials and improving sensitivity for true errors and specificity for false rejection. The proposed mNL-PBRTQC further targets limitations in generalizability across different settings by using nonlinear regression–adjusted machine learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the mNL-PBRTQC model built and validated?",{"text":80,"@type":76},"Artificial biases were added via computer simulation to patient data for 10 measurands. The model was trained using eight hospital laboratory databases and validated using independent patient datasets from three other hospitals.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main findings from the independent test datasets?",{"text":84,"@type":76},"In all three independent datasets, mNL-PBRTQC outperformed the IFCC PBRTQC approach and linear regression-adjusted real-time quality control for all measurands and all applied biases, with specific behavior such as smaller uncertainty around the median for 20% bias in the platelet example.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]