An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set

2021 ◽  
Vol 131 ◽  
pp. 104242
Author(s):  
Yan Liu ◽  
Jihui Chen ◽  
Yin You ◽  
Ajing Xu ◽  
Ping Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Incomplete Data ◽  
Maintenance Dose ◽  
Data Set ◽  
Warfarin Maintenance Dose

Symmetry Breaking and Training from Incomplete Data with Radial Basis Boltzmann Machines

1997 ◽  
Vol 08 (03) ◽  
pp. 301-315 ◽  
Author(s):  
Marcel J. Nijman ◽  
Hilbert J. Kappen
Keyword(s):  
Symmetry Breaking ◽  
Incomplete Data ◽  
Missing Values ◽  
Nearest Neighbor ◽  
Boltzmann Machine ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Input Space ◽  
Learning Rules ◽  
Radial Basis

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

scholarly journals Influence of NQO1 Polymorphisms on Warfarin Maintenance Dose: A Systematic Review and Meta-Analysis (rs1800566 and rs10517)

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lihong Tian ◽  
Pingping Xiao ◽  
Bingrong Zhou ◽  
Yishan Chen ◽  
Lijuan Kang ◽  
...  
Keyword(s):  
Maintenance Dose ◽  
Meta Analysis ◽  
Warfarin Dose ◽  
Maintenance Dosage ◽  
Sensitivity Analyses ◽  
Cochrane Library ◽  
The Cochrane Library ◽  
The Relationship ◽  
Review Manager ◽  
Warfarin Maintenance Dose

This meta-analysis was conducted to analyze the effect of NQO1 polymorphism on the warfarin maintenance dosage. Using strict inclusion and exclusion criteria, we searched PubMed, EMBASE, and the Cochrane Library for eligible studies published prior to July 7, 2021. The required data were extracted, and experts were consulted when necessary. Review Manager Version 5.4 software was used to analyze the relationship between NQO1 polymorphisms and the warfarin maintenance dosage. Four articles involving 757 patients were included in the meta-analysis. Patients who were NQO1 rs10517 G carriers (AG carriers or GG carriers) required a 48% higher warfarin maintenance dose than those who were AA carriers. Patients with NQO1 rs1800566 CT carriers required a 13% higher warfarin dose than those who were CC carriers, with no associations observed with the other comparisons of the NQO1 rs1800566 genotypes. However, the results obtained by comparing the NQO1 rs1800566 genotypes require confirmation, as significant changes in the results were found in sensitivity analyses. Our meta-analysis suggests that the NQO1 rs10517and NQO1 rs1800566 variant statuses affect the required warfarin maintenance dose.

The effect of the plasma levels of proteins C and S on the prediction of warfarin maintenance dose requirements

1992 ◽  
Vol 52 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Aharon Lubetsky ◽  
Uri Seligsohn ◽  
David Ezra ◽  
Hillel Halkin
Keyword(s):  
Plasma Levels ◽  
Maintenance Dose ◽  
Warfarin Maintenance Dose

Data Mining with Incomplete Data

2011 ◽  
pp. 293-296 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).

scholarly journals Estimating the Entropy for Lomax Distribution Based on Generalized Progressively Hybrid Censoring

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1219 ◽  
Author(s):  
Shuhan Liu ◽  
Wenhao Gui
Keyword(s):  
Incomplete Data ◽  
Real Data ◽  
Unknown Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Lomax Distribution ◽  
Bayesian Estimates ◽  
Squared Error ◽  
General Entropy Loss Function ◽  
Two Parameter

As it is often unavoidable to obtain incomplete data in life testing and survival analysis, research on censoring data is becoming increasingly popular. In this paper, the problem of estimating the entropy of a two-parameter Lomax distribution based on generalized progressively hybrid censoring is considered. The maximum likelihood estimators of the unknown parameters are derived to estimate the entropy. Further, Bayesian estimates are computed under symmetric and asymmetric loss functions, including squared error, linex, and general entropy loss function. As we cannot obtain analytical Bayesian estimates directly, the Lindley method and the Tierney and Kadane method are applied. A simulation study is conducted and a real data set is analyzed for illustrative purposes.

Incomplete Big Data Distributed Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1496-1499
Author(s):  
Yong Lin Leng
Keyword(s):  
Big Data ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Design Method ◽  
Complete Data ◽  
Similarity Metrics ◽  
Distributed Clustering ◽  
Computing Technology ◽  
Data Set ◽  
Affinity Propagation Clustering

Partially missing or blurring attribute values make data become incomplete during collecting data. Generally we use inputation or discarding method to deal with incomplete data before clustering. In this paper we proposed an a new similarity metrics algorithm based on incomplete information system. First algorithm divided the data set into a complete data set and non complete data set, and then the complete data set was clustered using the affinity propagation clustering algorithm, incomplete data according to the design method of the similarity metric is divided into the corresponding cluster. In order to improve the efficiency of the algorithm, designing the distributed clustering algorithm based on cloud computing technology. Experiment demonstrates the proposed algorithm can cluster the incomplete big data directly and improve the accuracy and effectively.

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