Research On the Data Quality Control Model of the Traditional Chinese Medicine Inpatient Medical Record Home Page Based on XGBoost

2021 ◽  
Author(s):  
Weidong Pan ◽  
Jiadong Xie ◽  
Yufeng Zhao ◽  
Baoyan Liu ◽  
Kongfa Hu
Keyword(s):  
Quality Control ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Data Quality ◽  
Medical Record ◽  
Control Model ◽  
Home Page ◽  
Data Quality Control

scholarly journals Pembuatan Model Kendali Mutu Data Sedimen

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Sri Mulat Yuningsih ◽  
Asep Ferdiansyah ◽  
Muhammad Fauzi
Keyword(s):  
Quality Control ◽  
Sediment Transport ◽  
Data Quality ◽  
River Flow ◽  
Control Model ◽  
Special Treatment ◽  
Data Quality Control ◽  
Discharge Data ◽  
Sediment Rating Curve ◽  
Sediment Data

Special treatment for watershed management was needed due to severe of watershed condition in most regions in Indonesia. The treatment should be directed to comprehensive changes of management paradigm for all aspects in it. Those were indicated by the increasing of disasters around the watershed, such as floods, droughts, landslides, erosion and increased of sediment transported by the river basin. The increasing of sedimentation which occurs in the river flow will disrupt the performance of existing hydraulic structure in the river. The event could be monitored by hydrological data, especially with the continuously and accurately of discharge and sediment data. In order to solve the problem, sediment data quality control model was needed. The purpose of this research is to determined suspended sediment data quality control model, in order to have continuous and quality guaranteed of sediment transport data. The scopes of this sediment data quality control were making criteria and sub, determining rank priority between criteria and sub, arranging scoring form, trial and error, finalization. The model consists of three main stages, there are measurement of discharge and taking sediment sample (QC1), drawing of sediment rating curve (QC2), and conversion of discharge data to sediment transport (QC3).

Best practice in the management of clinical coding services: Insights from a project in the Republic of Ireland, Part 1

2017 ◽  
Vol 46 (2) ◽  
pp. 69-77 ◽  
Author(s):  
Beth A Reid ◽  
Lee Ridoutt ◽  
Paul O’Connor ◽  
Deirdre Murphy
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Medical Record ◽  
Best Practice ◽  
Control Measures ◽  
Data Quality Control ◽  
Clinical Coding ◽  
Republic Of Ireland ◽  
The Republic

Introduction: This article presents some of the results of a year-long project in the Republic of Ireland to review the quality of the hospital inpatient enquiry data for its use in activity-based funding (ABF). This is the first of two papers regarding best practice in the management of clinical coding services. Methods: Four methods were used to address this aspect of the project, namely a literature review, a workshop, an assessment of the coding services in 12 Irish hospitals by structured interviews of the clinical coding managers, and a medical record audit of the clinical codes in 10 hospitals. Results: The results included here are those relating to the quality of the medical records, coding work allocation and supervision processes, data quality control measures, communication with clinicians, and the visibility of clinical coders, their managers, and the coding service. Conclusion: The project found instances of best practice in the study hospitals but also found several areas needing improvement. These included improving the structure and content of the medical record, clinician engagement with the clinical coding teams and the ABF process, and the use of data quality control measures.

Penerapan model kendali mutu data hidrologi dalam rangka peningkatan kualitas data

2018 ◽  
Vol 13 (2) ◽  
pp. 131-146
Author(s):  
Mirwan Rofiq Ginanjar ◽  
Sri Mulat Yuningsih
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Water Resources ◽  
Data Quality ◽  
Control Model ◽  
Rainfall Data ◽  
Data Quality Control ◽  
Discharge Data ◽  
Hydrological Data

Planning and management of water resources are dependent on the quality of hydrological data. Hydrological data plays an important role in hydrological analysis. The availability of good and qualified hydrological data is one of the determinants of the results of hydrological analysis. However, the facts indicate that many of the available data do not fit their ideal state. To solve this problem, a hydrological data quality control model should be established in order to improve the quality of national hydrological data. The scope includes quality control of rainfall and discharge data. Analysis of the quality control of rainfall data was conducted on 58 rainfall stations spread on the island of Java. The analysis shows that 41 stations are good categorized, 14 stations are in moderate category and 3 stations are badly categorized. Based on these results, a light improvement scenario was performed, good category Station increased to 46 stations, moderate category decreased to 11 stations and bad category reduced to 1 Stations. Quality control of discharge data analysis was conducted on 14 discharge stations spread on Java Island. Analyzes were performed for QC1, QC2 and QC3 then got final QC value. The results on the final QC show no stations for good category, 2 stations for moderate categories and 12 stations for bad category. Based on the results of the analysis, a light improvement scenario was performed with the result of bad category increased to good category 5 stations, bad category increased to moderate 7 stations, and moderate category 1 stations.

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals An interactive graphical system for XBT data quality control and visualization

2001 ◽  
Vol 27 (7) ◽  
pp. 867-876 ◽  
Author(s):  
Pankajakshan Thadathil ◽  
Aravind K Ghosh ◽  
J.S Sarupria ◽  
V.V Gopalakrishna
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Graphical System
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