Neural Networks Ensemble for Cyclosporine Concentration Monitoring

Camps, G.; Soria, Emilio; Guerrero, José David Martín; Serrano, Adela; Ruixo, Juan José Pérez; Jiménez, N. V.

doi:10.1007/3-540-44668-0_98

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…After removing duplicated articles, 3,346 studies were screened by the title and/or abstract, 3,175 irrelevant studies were excluded and 171 articles were included for full‐text review. Finally, 64 articles related to precision dosing using ML were included for analysis 11–74 . The PRISMA flow diagram representing the study selection process and review results is presented in Figure .…”

Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N=23, 35.9%) and anti‐infectives (N=21, 32.8%), and there is currently only very limited attention for special populations such as children (N=22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Machine Learning: A New Approach for Dose Individualization

Li,

Tang,

et al. 2023

Clin Pharma and Therapeutics

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“…We used three neural models for the prediction (Multilayer Perceptron, FIR network and Elman network), and we also considered combined forecasts. We focused on simple linear combinations as in the EPO case study; all of them are extensively described in (Camps et al, 2001(Camps et al, , 2003. The FIR network shows the best outcomes among the single neural models (RMSE ¼ 52.34 ng/ml), but with OLC neural-network ensembles (Hashem, 1997), outcomes clearly improved, since a reduction of 14.46% is observed in RMSE.…”

Section: Prediction Of Cyclosporine Blood Concentrationmentioning

confidence: 99%

Neural networks as effective techniques in clinical management of patients: some case studies

Martín

Soria

Camps

et al. 2004

Transactions of the Institute of Measurement and Control

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In this paper, we present four examples of effective implementation of neural systems in the daily clinical practice. There are two main goals in this work; the first one is to show that neural networks are especially well-suited tools for solving different kind of medical/pharmaceutical problems, given the complex inputÀoutput relationships and the few a priori knowledge about data distribution and variable relations. The second goal is to develop specific software applications, which enclose complex mathematical models, to clinicians; thus, the use of such models as decision support systems is facilitated. Four important pharmaceutical problems are considered in this study: identification of patients with potential risk of postchemotherapy emesis, classification of patients depending on their risk of digoxin intoxication, prediction of cyclosporine A through concentration and prediction of erythropoietin blood concentrations. The Multilayer Perceptron in classification problems and dynamic neural networks, such as the Elman recurrent neural network and the Finite Impulse Response neural network in prediction problems, have been used. Moreover, network ensembles of different kind of networks have been taken into account. Results show that neural networks are suitable tools for medical classification and prediction tasks, outperforming the mostly used methods in these problems (logistic regression and multivariate analysis).

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Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks

Camps‐Valls

Porta-Oltra

Soria-Olivas

et al. 2003

IEEE Trans. Biomed. Eng.

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This paper proposes the use of neural networks for individualizing the dosage of cyclosporine A (CyA) in patients who have undergone kidney transplantation. Since the dosing of CyA usually requires intensive therapeutic drug monitoring, the accurate prediction of CyA blood concentrations would decrease the monitoring frequency and, thus, improve clinical outcomes. Thirty-two patients and different factors were studied to obtain the models. Three kinds of networks (multilayer perceptron, finite impulse response (FIR) network, and Elman recurrent network) and the formation of neural-network ensembles are used in a scheme of two chained models where the blood concentration predicted by the first model constitutes an input to the dosage prediction model. This approach is designed to aid in the process of clinical decision making. The FIR network, yielding root-mean-square errors (RMSEs) of 52.80 ng/mL and mean errors (MEs) of 0.18 ng/mL in validation (10 patients) showed the best blood concentration predictions and a committee of trained networks improved the results (RMSE = 46.97 ng/mL, ME = 0.091 ng/mL). The Elman network was the selected model for dosage prediction (RMSE = 0.27 mg/Kg/d, ME = 0.07 mg/Kg/d). However, in both cases, no statistical differences on the accuracy of neural methods were found. The models' robustness is also analyzed by evaluating their performance when noise is introduced at input nodes, and it results in a helpful test for models' selection. We conclude that neural networks can be used to predict both dose and blood concentrations of cyclosporine in steady-state. This novel approach has produced accurate and validated models to be used as decision-aid tools.

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Neural Networks Ensemble for Cyclosporine Concentration Monitoring

Cited by 5 publications

References 10 publications

Machine Learning: A New Approach for Dose Individualization

Machine Learning: A New Approach for Dose Individualization

Neural networks as effective techniques in clinical management of patients: some case studies

Prediction of cyclosporine dosage in patients after kidney transplantation using neural networks

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