Construction of a risk prediction model of vancomycin-associated nephrotoxicity to be used at the time of initial therapeutic drug monitoring: A data mining analysis using a decision tree model

2018 ◽  
Vol 25 (1) ◽  
pp. 163-170 ◽  
Author(s):  
Shungo Imai ◽  
Takehiro Yamada ◽  
Kumiko Kasashi ◽  
Yusuke Niinuma ◽  
Masaki Kobayashi ◽  
...  
Keyword(s):  
Data Mining ◽  
Therapeutic Drug Monitoring ◽  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Drug Monitoring ◽  
Risk Prediction Model ◽  
Decision Tree Model ◽  
Therapeutic Drug ◽  
Tree Model

scholarly journals A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Yuya Asanomi ◽  
Keith A. Boroevich ◽  
Alok Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Mirna Expression ◽  
Dementia With Lewy Bodies ◽  
Target Genes ◽  
Lewy Bodies ◽  
Risk Prediction Model ◽  
Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

An Educational Data Mining Application by Using Multiple Intelligences

2020 ◽  
pp. 93-110
Author(s):  
Esra Aksoy ◽  
Serkan Narli ◽  
Mehmet Akif Aksoy
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Learning Styles ◽  
Multiple Intelligences ◽  
Gifted Students ◽  
Personality Types ◽  
Decision Tree Model ◽  
Tree Model ◽  
Educational Domain ◽  
Using Data

The aim of this chapter is to illustrate both uses of data mining methods and the way of these methods can be applied in education by using students' multiple intelligences. Data mining is a data analysis methodology that has been successfully used in different areas including the educational domain. In this context, in this study, an application of EDM will be illustrated by using multiple intelligence and some other variables (e.g., learning styles and personality types). The decision tree model was implemented using students' learning styles, multiple intelligences, and personality types to identify gifted students. The sample size was 735 middle school students. The constructed decision tree model with 70% validity revealed that examination of mathematically gifted students using data mining techniques may be possible if specific characteristics are included.

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