Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

Shaikhina, Torgyn; Lowe, David; Daga, Sunil; Briggs, David; Higgins, Robert; Khovanova, N. A.

doi:10.1016/j.bspc.2017.01.012

Cited by 244 publications

(122 citation statements)

References 24 publications

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“…The comparison of predictive performances between our classifier and classifiers in the literature underlines its relevance to pre-kidney transplant risk assessment (Supplemental Table 5). Its accuracy of 82.7% is i) one of the highest among all donor-independent risk assessment models (19,25,30,34,57), ii) comparable to any AR models (19,20,30,34,35,57,58,21,22,(24)(25)(26)(27)(28)(29) and iii) comparable to any SAB data based models for ABMR (22,(24)(25)(26)(27)(28). Furthermore, our classifier is based on SAB, an established diagnostics laboratory tool, thereby facilitating its further use for ACR risk assessment.…”

Section: Discussionmentioning

confidence: 66%

“…For early risk assessment, the large majority of models are donordependent, as they either employ measurements from the early post-transplantation period or utilize donor -derived data (e.g. from crossmatch tests) (19,20,29,30,(21)(22)(23)(24)(25)(26)(27)(28). The most common approach for pre-transplant risk assessment relies on the characterization of HLA antibodies in recipient serum samples by solid phase single HLA antigen bead (SAB) assay (22)(23)(24)(25)(26)(27)31).…”

Section: Introductionmentioning

confidence: 99%

“…The assay facilitates detection and identification of anti-HLA antibody specificities and provides a method for monitoring the development of donor-specific antibodies (DSA). The detection of DSA through SAB assays is a well-established method for antibody-mediated rejection (ABMR) pre-transplantation risk assessment, but not for ACR (22)(23)(24)(25)(26)(27)(28)32). 5 Approaches for risk assessment of ACR do not employ DSA for the prediction -as both patients with or without DSA experience episodes of ACR -but other risk markers, such as soluble CD30 levels or panel of reactive T cells (21,(33)(34)(35).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection

Wittenbrink

Herrmann

Blázquez-Navarro

et al. 2018

Preprint

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SummaryAcute cellular rejection (ACR) is associated with complications after kidney transplantation, such as graft dysfunction and graft loss. Early risk assessment is therefore critical for the improvement of transplantation outcomes. In this work, we retrospectively analyzed a pre-transplant HLA antigen bead assay data set that was acquired by the e:KID consortium as part of a systems medicine approach. The data set included single antigen bead (SAB) reactivity profiles of 52 low-risk graft recipients (negative complement dependent cytotoxicity crossmatch, PRA<30%) who showed detectable pre-transplant anti-HLA 1 antibodies. To assess whether the reactivity profiles provide a means for ACR risk assessment, we established a novel approach which differs from standard approaches in two aspects: the use of quantitative continuous data and the use of a multiparameter classification method. Remarkably, it achieved significant prediction of the 38 graft recipients who experienced ACR with a balanced accuracy of 82.7% (sensitivity=76.5%, specificity= 88.9%). The resultant classifier achieved one of the highest prediction accuracies in the literature for pre-transplant risk assessment of ACR. Importantly, it can facilitate risk assessment in non-sensitized patients who lack donor-specific antibodies. As the classifier is based on continuous data and includes weak signals, our results emphasize that not only strong but also weak binding interactions of antibodies and HLA 1 antigens contain predictive information.

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Section: Discussionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection

Wittenbrink

Herrmann

Blázquez-Navarro

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…classes) of a single sample using a logistic function [20]. In a multi-class scenario, the class with the highest probability denotes the predicted class [22]. Decision Trees are non-parametric classification models by learning simple decision rules from features to predict class value [20].…”

Section: Predictive Classification Modelsmentioning

confidence: 99%

A machine learning approach for Colles’ fracture treatment diagnosis

Ngan

Garcez

Knapp

et al. 2020

Preprint

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Wrist fractures (e.g. Colles' fracture) are the most common injuries in the upper extremity treated in Emergency Departments. Most patients are treated with a procedure called Manipulation under Anaesthesia. Surgical treatment may still be needed in complex fractures or if the wrist stability is not restored. This can lead to inefficiency in constrained medical resources and patients' inconvenience. Previous geometric measurements in X-ray images were found to provide statistical differences between healthy controls and fractured cases as well as preand post-intervention images. The most discriminating measurements were associated with the texture analysis of the radial bone. This work presents further analysis of these measurements and applying them as features to identify the best machine learning model for Colles' fracture treatment diagnosis. Random forest was evaluated to be the best model based on validation accuracy. The non-linearity of the measurement features has attributed to the superior performance of an ensembled tree-based model. It is also interesting that the most important features (i.e. texture and swelling) required in the optimised random forest model are consistent with previous findings.

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“…Another study shows the use of SVMs for predicting medication adherence in heart-failure patients [26]. A Decision Tree model has been used to predict early rejection in kidney transplant [27]. More recently, Artificial Neural Networks (ANNs) and models based on their framework are preferred for applications that include audial or/and visual data because of the high dimensionality and complexity of images, videos and recordings.…”

Section: Introductionmentioning

confidence: 99%

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

Besler

Wang

Chan

et al. 2019

International Journal of Hyperthermia

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Background: Radiofrequency ablation is a minimally-invasive treatment method that aims to destroy undesired tissue by exposing it to alternating current in the 100 kHz-800 kHz frequency range and heating it until it is destroyed via coagulative necrosis. Ablation treatment is gaining momentum especially in cancer research, where the undesired tissue is a malignant tumor. While ablating the tumor with an electrode or catheter is an easy task, real-time monitoring the ablation process is a must in order to maintain the reliability of the treatment. Common methods for this monitoring task have proven to be accurate, however, they are all time-consuming or require expensive equipment, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. Methods: A machine learning (ML) approach is presented that aims to reduce the monitoring time while keeping the accuracy of the conventional methods. Two different hardware setups are used to perform the ablation and collect impedance data at the same time and different ML algorithms are tested to predict the ablation depth in 3 dimensions, based on the collected data. Results: Both the random forest and adaptive boosting (adaboost) models had over 98% R 2 on the data collected with the embedded system-based hardware instrumentation setup, outperforming Neural Network-based models. Conclusions: It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a Â86-64 workstation.

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Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

Cited by 244 publications

References 24 publications

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection

A novel approach reveals that HLA class 1 single antigen bead-signatures provide a means of high-accuracy pre-transplant risk assessment of acute cellular rejection

A machine learning approach for Colles’ fracture treatment diagnosis

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

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