Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Junior, José Medeiros de Araújo; Menezes, José Maria P.; Albuquerque, Alberto Alexandre Moura de; Almeida, Otacílio da Mota; Araújo, Fábio Meneghetti Ugulino de

doi:10.3390/s131115613

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…Sharing this data with large commercial players who have the programming and processing ability to extract multiple signals from that data is even more difficult [27]. Only three papers included in our review addressed use of machine learning to identify data errors [28][29][30]. Errors are common in routine EHR data [6], and thus, datasets must be cleaned before analyses.…”

Section: Results In Context With Literaturementioning

confidence: 99%

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

et al. 2019

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Background Intensive care units (ICUs) face financial, bed management, and staffing constraints. Detailed data covering all aspects of patients’ journeys into and through intensive care are now collected and stored in electronic health records: machine learning has been used to analyse such data in order to provide decision support to clinicians. Methods Systematic review of the applications of machine learning to routinely collected ICU data. Web of Science and MEDLINE databases were searched to identify candidate articles: those on image processing were excluded. The study aim, the type of machine learning used, the size of dataset analysed, whether and how the model was validated, and measures of predictive accuracy were extracted. Results Of 2450 papers identified, 258 fulfilled eligibility criteria. The most common study aims were predicting complications (77 papers [29.8% of studies]), predicting mortality (70 [27.1%]), improving prognostic models (43 [16.7%]), and classifying sub-populations (29 [11.2%]). Median sample size was 488 (IQR 108–4099): 41 studies analysed data on > 10,000 patients. Analyses focused on 169 (65.5%) papers that used machine learning to predict complications, mortality, length of stay, or improvement of health. Predictions were validated in 161 (95.2%) of these studies: the area under the ROC curve (AUC) was reported by 97 (60.2%) but only 10 (6.2%) validated predictions using independent data. The median AUC was 0.83 in studies of 1000–10,000 patients, rising to 0.94 in studies of > 100,000 patients. The most common machine learning methods were neural networks (72 studies [42.6%]), support vector machines (40 [23.7%]), and classification/decision trees (34 [20.1%]). Since 2015 (125 studies [48.4%]), the most common methods were support vector machines (37 studies [29.6%]) and random forests (29 [23.2%]). Conclusions The rate of publication of studies using machine learning to analyse routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods. Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice. Electronic supplementary material The online version of this article (10.1186/s13054-019-2564-9) contains supplementary material, which is available to authorized users.

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Section: Results In Context With Literaturementioning

confidence: 99%

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

et al. 2019

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“…(3) applying the proposed architecture to identify reliable dynamical models to be used in advanced control strategies, such as the predictive controllers; (4) evaluating the feasibility to apply the FWNN-MCC as an inferential system to estimate chemical compositions, calibrate sensors [62], and fault diagnosis, among others.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Linhares

Fontes

Martins

et al. 2015

Mathematical Problems in Engineering

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Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS). In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE) cost function is replaced by the Maximum Correntropy Criterion (MCC) in the traditional error backpropagation (BP) algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

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“…Caso essas temperaturas não estejam dentro dos limites estabelecidos pela norma, deve ser executada a manutenção da incubadora. Para isto, são realizados vários testes, nos quais sensores físicos são inseridos em seu interior, sendo necessário retirar a incubadora de operação [de Araújo et al 2013].…”

Section: Introductionunclassified

“…Dentre os modelos propostos, ARMA, RNA, ARMAX-RNA, esteúltimo se mostrou o mais promissor, com menor desvio padrão em todos os experimentos realizados. O trabalho de [de Araújo et al 2013], propõe um novo procedimento de inferência baseado em redes neurais, com o objetivo de avaliar e calibrar a temperatura e umidade de uma incubadora neonatal. Para isso, foram apresentadas as vantagens obtidas desde a redução do número de sensores até a eficácia do método empregado, que alcançou baixos valores de erros em relação aos valores reais.…”

Section: Introductionunclassified

Inferência da Temperatura Interna em uma Incubadora Neonatal Utilizando Redes Neurais Artificiais

Rocha¹,

Perci²,

Soares³

et al. 2020

Anais Da Escola Regional De Computação Ceará, Maranhão, Piauí (ERCEMAPI 2020)

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A incubadora neonatal consiste em um equipamento hospitalar que proporciona condições de temperatura adequadas aos recém-nascidos. Este trabalho propõe um sistema de inferência neural, obtido a partir de redes neurais artificiais (RNA) do tipo MultiLayer Perceptron (MLP), que será utilizado como sensor virtual. Esse sistema substitui a instalação de sensores físicos durante a certificação ou manutenção da incubadora e tornará possível a realização do monitoramento on-line da temperatura. Os resultados demonstraram que o sistema pode ser utilizado satisfatoriamente como sensor virtual. Foram obtidos erros médios quadráticos de validação entre 1.6x10-3 e 1.9x10-3 e variância entre 2.533x10-7 e 8.098x10-7.

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Assessment and Certification of Neonatal Incubator Sensors through an Inferential Neural Network

Cited by 12 publications

References 17 publications

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Use of machine learning to analyse routinely collected intensive care unit data: a systematic review

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Inferência da Temperatura Interna em uma Incubadora Neonatal Utilizando Redes Neurais Artificiais

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