Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction

Shirwaikar, Rudresh Deepak; Acharya, Dinesh U; Makkithaya, Krishnamoorthi; Surulivelrajan, M; Srivastava, Shikhar; U, Leslie Edward S Lewis

doi:10.1016/j.artmed.2019.07.008

Cited by 38 publications

(18 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…β = (H T ΛH+λʹI) -1 H T ΛT (11) where the λʹ = 2Nλ and Λ is the diagonal matrix with elements calculated as follows: With two coefficients, Equation 9gives a more accurate estimation of the costs of the output layer, leading to a higher robustness of the model. Although Equations (7) and (9) can acquire better local similarity measurements compared with Equation (5), both criterions limit the correntropy into two kernels, leading to an inappropriate description on the probability distribution of the data.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

“…With the rapid development of powerful computing environments and rich data sources, artificial intelligence (AI) technology such as neural networks [1][2][3], adaptive filtering [4][5][6] and evolutionary algorithms [7][8][9] has become increasingly more applicable for forecasting problems in various scenarios, such as medicine [10][11][12], economy [13][14][15] and electronic engineering [16][17][18]. The methods have acquired high reputations due to their great approximation abilities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

View full text Add to dashboard Cite

In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

show abstract

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…The algorithm works by updating the parameters which are to be optimized to gradient of the value function. Stochastic gradient descent or SGD, is a variant of gradient descent optimization method that each iteration minimizes the error by considering the error for each data point [24]. The iterations of SGD can be described as [15]:…”

Section: Gradient Descentmentioning

confidence: 99%

Human Activity Prediction using Long Short Term Memory

2020

View full text Add to dashboard Cite

Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.

show abstract

“…Artificial intelligence, particularly Artificial Neural Networks (ANN) are robust methodologies for the forecasting of diagnosis/prognosis with high predictive accuracy, and nowadays are used to support medical decisions in NICUs (17,18). Different computational models have been developed to predict adult, pediatric and neonatal sepsis (19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Helguera-Repetto

Soto-Ramirez

Villavicencio-Carrisoza

et al. 2020

Front. Pediatr.

View full text Add to dashboard Cite

Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early-and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early-and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.

show abstract

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction

Cited by 38 publications

References 8 publications

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Human Activity Prediction using Long Short Term Memory

Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Contact Info

Product

Resources

About