Paediatric reference intervals for ionised calcium – a data mining approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jason Zhi Yong Chung
Keyword(s):  
Data Mining ◽  
Reference Intervals ◽  
Data Mining Approach ◽  
Ionised Calcium

Establishment of Reference Intervals for Alkaline Phosphatase in Pakistani Children Using a Data Mining Approach

2019 ◽  
Vol 51 (5) ◽  
pp. 484-490 ◽  
Author(s):  
Sibtain Ahmed ◽  
Jakob Zierk ◽  
Aysha Habib Khan
Keyword(s):  
Data Mining ◽  
Alkaline Phosphatase ◽  
Clinical Decision Making ◽  
Clinical Chemistry ◽  
Age Groups ◽  
Clinical Decision ◽  
German Society ◽  
Reference Intervals ◽  
Photometric Method ◽  
Data Mining Approach

Abstract Objective To establish reference intervals (RIs) for alkaline phosphatase (ALP) levels in Pakistani children using an indirect data mining approach. Methods ALP levels analyzed on a Siemens Advia 1800 analyzer using the International Federation of Clinical Chemistry’s photometric method for both inpatients and outpatients aged 1 to 17 years between January 2013 and December 2017, including patients from intensive care units and specialty units, were retrieved. RIs were calculated using a previously validated indirect algorithm developed by the German Society of Clinical Chemistry and Laboratory Medicine’s Working Group on Guide Limits. Results From a total of 108,845 results, after the exclusion of patients with multiple specimens, RIs were calculated for 24,628 males and 18,083 females with stratification into fine-grained age groups. These RIs demonstrate the complex age- and sex-related ALP dynamics occurring during physiological development. Conclusion The population-specific RIs serve to allow an accurate understanding of the fluctuations in analyte activity with increasing age and to support clinical decision making.

A Data-Mining Approach for Energy Behavioural Analysis to Ease Predictive Modelling for the Smart City

2018 ◽  
Author(s):  
Lavinia Chiara Tagliabue ◽  
Stefano Rinaldi ◽  
Mario Favalli Ragusini ◽  
Giovanni Tardioli ◽  
Angelo Luigi Camillo Ciribini
Keyword(s):  
Data Mining ◽  
Smart City ◽  
Predictive Modelling ◽  
Data Mining Approach ◽  
Behavioural Analysis

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction

Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

2021 ◽  
Vol 39 ◽  
pp. 102246
Author(s):  
Junqi Wang ◽  
Jin Hou ◽  
Jianping Chen ◽  
Qiming Fu ◽  
Gongsheng Huang
Keyword(s):  
Data Mining ◽  
Optimal Control ◽  
Hvac Systems ◽  
Data Mining Approach ◽  
Event Driven

scholarly journals Data Mining Approach for Learning management system

2021 ◽  
Vol 1088 (1) ◽  
pp. 012013
Author(s):  
Harry Dhika ◽  
Fitriana Destiawati ◽  
Surajiyo ◽  
Musa Jaya
Keyword(s):  
Data Mining ◽  
Management System ◽  
Learning Management System ◽  
Data Mining Approach ◽  
Learning Management

scholarly journals Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoko Hiura ◽  
Shige Koseki ◽  
Kento Koyama
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Listeria Monocytogenes ◽  
Water Activity ◽  
Bacterial Population ◽  
Gradient Boosting ◽  
Initial Cell ◽  
Data Mining Approach ◽  
Cell Counts ◽  
Extreme Gradient Boosting

AbstractIn predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 °C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: ‘time’, ‘temperature’, ‘pH’, ‘water activity’, ‘initial cell counts’, ‘whether the viable count is initial cell number’, and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data.

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