Introduction: Seizure is a transient phenomenon with genesis in excessive abnormal or synchronous neuronal electrical activity in the brain, while epilepsy is defined as a brain dysfunction characterized by persistent predisposition to generate seizures. The identification of epileptogenic electroencephalographic patterns can be performed using machine learning.the present study aimed to develop a transfer learning based classifier able to detect epileptic seizures in images generated from electroencephalographic data graphic representation.Material and Methods: We used the Epileptic Seizure Recognition Data Set,which consists of 500 brain activity records for 23.6 seconds comprising 23 chunks of 178 data points, and transformed the resulting 11500 instances into images by graphically plotting its data points. Those images were then splitted in training and test set and used to build and assess, respectvely, a transfer learning-based deep neural network, which classified the images according the presence or absence of epileptic seizures.Results: The model achieved 100% accuracy, sensitivity and specificity, with a AUC-score of 1.0, demonstrating the great potential of transfer learning for the analysis of graphically represented electroencephalographic data.Conclusion: It is opportune to raise new studies involving transfer learning for the analysis of signal data, with the aim of improving, disseminating and validating its use for daily clinical practice.
BACKGROUND Intensive care unit (ICU) patients demand continuous monitoring of several clinical and laboratory parameters that directly influence their medical progress and the staff’s decision-making. Those data are vital in the assistance of these patients, being already used by several scoring systems. In this context, machine learning approaches have been used for medical predictions based on clinical data, which includes patient outcomes. AIM To develop a binary classifier for the outcome of death in ICU patients based on clinical and laboratory parameters, a set formed by 1087 instances and 50 variables from ICU patients admitted to the emergency department was obtained in the “WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction” dataset. METHODS For categorical variables, frequencies and risk ratios were calculated. Numerical variables were computed as means and standard deviations and Mann-Whitney U tests were performed. We then divided the data into a training (80%) and test (20%) set. The training set was used to train a predictive model based on the Random Forest algorithm and the test set was used to evaluate the predictive effectiveness of the model. RESULTS A statistically significant association was identified between need for intubation, as well predominant systemic cardiovascular involvement, and hospital death. A number of the numerical variables analyzed (for instance Glasgow Coma Score punctuations, mean arterial pressure, temperature, pH, and lactate, creatinine, albumin and bilirubin values) were also significantly associated with death outcome. The proposed binary Random Forest classifier obtained on the test set ( n = 218) had an accuracy of 80.28%, sensitivity of 81.82%, specificity of 79.43%, positive predictive value of 73.26%, negative predictive value of 84.85%, F1 score of 0.74, and area under the curve score of 0.85. The predictive variables of the greatest importance were the maximum and minimum lactate values, adding up to a predictive importance of 15.54%. CONCLUSION We demonstrated the efficacy of a Random Forest machine learning algorithm for handling clinical and laboratory data from patients under intensive monitoring. Therefore, we endorse the emerging notion that machine learning has great potential to provide us support to critically question existing methodologies, allowing improvements that reduce mortality.
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