Random Forests Classification Analysis for the Assessment of Diagnostic Skill

Katz, James D.; Mamyrova, Gulnara; Guzhva, Olena; Furmark, Lena

doi:10.1177/1062860609354639

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Random forest methods were generated in R using the party package and the cforest unbiased function, with 500 trees. Twenty‐five variables were evaluated as potential splitters at each node, representing approximately the square root of the number of independent variables . Following the methods of Strobl et al, individual conditional inference trees were constructed using bootstrap samples of the full data set.…”

Section: Methodsmentioning

confidence: 99%

Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

Taylor

Pare

Venkatesh

et al. 2016

Academic Emergency Medicine

396

284

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Objectives: Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big datadriven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case.Methods: This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics.Results: There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0. Conclusions:In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that

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Section: Methodsmentioning

confidence: 99%

Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

Taylor

Pare

Venkatesh

et al. 2016

Academic Emergency Medicine

396

284

View full text Add to dashboard Cite

show abstract

“…In the literature, there are abundant data analysis studies that make use of random forests classification scheme. Some disease diagnosis studies utilizing RF algorithm is given in [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Özçift

2011

J Med Syst

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Accurate classifiers are vital to design precise computer aided diagnosis (CADx) systems. Classification performances of machine learning algorithms are sensitive to the characteristics of data. In this aspect, determining the relevant and discriminative features is a key step to improve performance of CADx. There are various feature extraction methods in the literature. However, there is no universal variable selection algorithm that performs well in every data analysis scheme. Random Forests (RF), an ensemble of trees, is used in classification studies successfully. The success of RF algorithm makes it eligible to be used as kernel of a wrapper feature subset evaluator. We used best first search RF wrapper algorithm to select optimal features of four medical datasets: colon cancer, leukemia cancer, breast cancer and lung cancer. We compared accuracies of 15 widely used classifiers trained with all features versus to extracted features of each dataset. The experimental results demonstrated the efficiency of proposed feature extraction strategy with the increase in most of the classification accuracies of the algorithms.

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“…Its output is a voting decision by the outputs of all trees. This relatively young and simple machine-learning method has been implemented successfully in various fields of engineering and genetic screening (Guyon et al 2002; Diaz-Uriarte and de Andres 2006; Chen et al 2007;Wu et al 2008;Hanselmann et al 2009;Jiang et al 2009;Menze et al 2009;Katz et al 2010). Although there have been no reports yet, some scholars both at home and abroad have applied it to metabolomics data classification and metabolite screening.…”

Section: Supervised Methodsmentioning

confidence: 99%

Plant Metabolomics

2015

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Random Forests Classification Analysis for the Assessment of Diagnostic Skill

Cited by 7 publications

References 22 publications

Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

Enhanced Cancer Recognition System Based on Random Forests Feature Elimination Algorithm

Plant Metabolomics

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