Heart disease is one of the leading causes of mortality throughout the world. Among the different heart diagnosis techniques, an electrocardiogram (ECG) is the least expensive non-invasive procedure. However, the following are challenges: the scarcity of medical experts, the complexity of ECG interpretations, the manifestation similarities of heart disease in ECG signals, and heart disease comorbidity. Machine learning algorithms are viable alternatives to the traditional diagnoses of heart disease from ECG signals. However, the black box nature of complex machine learning algorithms and the difficulty in explaining a model’s outcomes are obstacles for medical practitioners in having confidence in machine learning models. This observation paves the way for interpretable machine learning (IML) models as diagnostic tools that can build a physician’s trust and provide evidence-based diagnoses. Therefore, in this systematic literature review, we studied and analyzed the research landscape in interpretable machine learning techniques by focusing on heart disease diagnosis from an ECG signal. In this regard, the contribution of our work is manifold; first, we present an elaborate discussion on interpretable machine learning techniques. In addition, we identify and characterize ECG signal recording datasets that are readily available for machine learning-based tasks. Furthermore, we identify the progress that has been achieved in ECG signal interpretation using IML techniques. Finally, we discuss the limitations and challenges of IML techniques in interpreting ECG signals.

https://www.mdpi.com/2075-4418/13/1/111/pdf?version=1672977172

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review

An increasing number of Internet application services are relying on encrypted traffic to offer adequate consumer privacy. Anomaly detection in encrypted traffic to circumvent and mitigate cyber se...

/pdf/anomaly-detection-in-encrypted-internet-traffic-using-hybrid-wj7kdlwm8n.pdf

Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning

In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistical features calculated by raw traffic directly. The statistical features can be used to supplement the information loss due to trimming. After that, the outputs from the CNN and the encoder in SAE are combined into new high-level features; these new features include the information from the trimmed raw traffic and statistical features. Finally, these new high-level features are used to classify encrypted traffic. “ISCX VPNnonVPN” traffic dataset is used to demonstrate the feasibility of this framework. The framework proposed in this paper can improve the performance of encrypted traffic classification; it achieves an f1-score of 0.98. Furthermore, new high-level features, which generated by combining the features extracted from a convolutional neural network and a stacked autoencoder, can represent different classes of traffic well. More importantly, this work is unique in the encrypted traffic classification field, for it is the first time to use both raw traffic and statistical features as the input of the model.

An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders

MTT: an efficient model for encrypted network traffic classification using multi-task transformer

BACKGROUND
To examine the relationship between the systemic immune-inflammation index (SII) and periodontitis and to investigate possible effect modifiers.


METHODS
Data used in the present cross-sectional study are from the National Health and Nutrition Examination Survey (NHANES) 2009-2014 (N = 10,301). The SII was calculated using the following formula: (neutrophils count × platelet count)/lymphocytes count. The category of periodontitis was defined by the Centers for Disease Control and Prevention and American Academy of Periodontology (CDC/AAP) classification. We employed natural cubic spline and multivariable logistic regression analyses to evaluate the associations of the SII with periodontitis.


RESULTS
The associations between SII and periodontal health followed a J-shape (p < 0.001). The risk of periodontitis tended to reduce with the increment of log2 (SII) in participants with log2 (SII) ≤ 8.66 (odds radio [OR] = 0.83; 95% CI: 0.69-0.999), especially among non-Hispanic Whites (OR = 0.70; 95% CI: 0.52-0.95), and increased with the increment of log2 (SII) in participants with log2 (SII) > 8.66 (OR = 1.19; 95% CI: 1.02-1.38). A similar trend was also observed between the SII and the number of sites with probing pocket depth (PPD) ≥4 mm and clinical attachment loss (CAL) ≥ 3 or 5 mm. Furthermore, we found a significantly stronger correlation between lymphocytes and either neutrophils or platelets in individuals with log2 (SII) > 8.66, as opposed to those with log2 (SII) ≤ 8.66.


CONCLUSIONS
There is a J-shaped association between SII and periodontitis in US adults, with an inflection point of log2 (SII) at 8.66, which may provide potential adjunctive treatment strategies for periodontitis with different immune response states. Further prospective trials are still required to confirm our findings.

J-shaped association between systemic immune-inflammation index and periodontitis: Results from NHANES 2009-2014.

With the widespread application of virtual private network (VPN) technology, real-time VPN traffic identification has become an increasingly important task in network management and security maintenance. Since traditional encrypted traffic identification technology is not effective in feature extraction and selection, this paper proposes two deep learning-based models to classify the traffic into VPN and non-VPN traffic, identify VPN traffic generated by six different applications much further. Our models utilize convolutional auto-encoding (CAE) and convolutional neural network (CNN), respectively, preprocessing the traffic samples into session pictures, to accomplish the experiment objectives. The CAE-based method, utilizing the unsupervised nature of CAE to extract the hidden layer features, can automatically learn the nonlinear relationship between original input and expected output. The CNN-based method performs well in extracting two-dimensional local features of images. Experimental results show that our models perform better than traditional identification methods. In the two-category identification, the best result comes from the CAE-based model; the overall identification accuracy rate is 98.77%. Among the six-category identification, the best result comes from CNN-based model; the overall identification accuracy rate is 92.92%.

Deep learning-based real-time VPN encrypted traffic identification methods

OBJECTIVE
Systemic immune-inflammation index (SII), a novel inflammatory indicator based on platelets, neutrophils and lymphocytes, has been shown to be associated with prognostic value in several solid tumors. However, its prognostic value in nonalcoholic fatty liver disease (NAFLD) has not been reported yet. Therefore, the present study aimed to investigate the prognostic value of SII in individuals with NAFLD.


METHODS
Data was collected from the 2005 to 2014 National Health and Nutrition Examination Survey (NHANES, https://www.cdc.gov/nchs/nhanes/index.htm), and vital status was derived from the National Death Index (NDI) up to 31 December 2015. NAFLD was diagnosed based on Hepatic Steatosis Index (HSI). Multivariate Cox regression and Kaplan-Meier survival curves were performed to measure the hazard ratios (HRs) and 95% confidence interval (CI). Our study investigated the relationship between SII and all-cause mortality by using two-part linear regression models with penalized splines, as well as Cox models with penalized splines.


RESULTS
A total of 10,787 NAFLD participants (44.14% men) aged ≥20 years old were enrolled. There were 776 deaths from all causes after a mean follow-up period of 5.6 years. According to the full adjusted Cox regression analysis, the low log2-SII group (quartile 1) and the highest log2-SII group (quartile 4) were significantly associated with increased mortality from all causes (aHR =1.86; 95% CI: 1.47-2.37; p < 0.0001). After controlling for confounders, an increase in log2-SII was associated with an increased all-cause mortality risk of 41% for every unit raised (aHR = 1.41; 95% CI: 1.26-1.57; p < 0.0001). After adjusting for multiple potential confounders, the association between log2-SII and all-cause mortality was nonlinear, and the threshold value was 8.8. There was no association between an increase of one unit in log2-SII and all-cause mortality below the threshold (aHR = 0.90, 95% CI: 0.71-1.15, p = 0.419). However, a higher log2-SII was associated with a higher risk of death from any cause when it exceeded the threshold (aHR = 1. 73, 95% CI: 1.49-2.02, p < 0.001).
Based on a study of US NAFLD patients, it was found that the baseline log2-SII is associated with all-cause mortality. Elevated SII is associated with poor survival among NAFLD patients.KEY MESSAGESUsing a large nationally representative survey of individuals among US adults, the study demonstrated that log2-SII was J-shaped and associated with all-cause death among individuals with NAFLD.Spline analyses demonstrated that the association between log2-SII and all-cause mortality was non-linear after adjusting for multiple potential confounders, and the threshold value was 8.8.Higher log2-SII associated with poor survival in NAFLD.

The systemic immune-inflammation index was non-linear associated with all-cause mortality in individuals with nonalcoholic fatty liver disease.

Deep learning approaches have exhibited a great ability on automatic interpretation of the electrocardiogram (ECG). However, large-scale public 12-lead ECG data are still limited, and the diagnostic labels are not uniform, which increases the semantic gap between clinical practice. In this study, we present a large-scale multi-label 12-lead ECG database with standardized diagnostic statements. The dataset contains 25770 ECG records from 24666 patients, which were acquired from Shandong Provincial Hospital (SPH) between 2019/08 and 2020/08. The record length is between 10 and 60 seconds. The diagnostic statements of all ECG records are in full compliance with the AHA/ACC/HRS recommendations, which aims for the standardization and interpretation of the electrocardiogram, and consist of 44 primary statements and 15 modifiers as per the standard. 46.04% records in the dataset contain ECG abnormalities, and 14.45% records have multiple diagnostic statements. The dataset also contains additional patient demographics. 

/pdf/a-large-scale-multi-label-12-lead-electrocardiogram-database-3ecn02o2.pdf

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements

/pdf/a-large-scale-multi-label-12-lead-electrocardiogram-database-11qavuh6.pdf

Objective This study aims to assess the performance of machine learning (ML)-based contrast-enhanced CT radiomics analysis for predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer. Methods This retrospective study analyzed 83 patients with breast cancer liver metastases. Radiomics features were extracted from arterial phase, portal venous phase, and delayed phase images, respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and validation sets consisted of 58 and 25 cases. Variance threshold, SelectKBest, and LASSO logistic regression model were employed for feature selection. The ML classifiers were K-nearest-neighbor algorithm (KNN), support vector machine (SVM), XGBoost, RF, LR, and DT, and the performance of classifiers was evaluated by ROC analysis. Results The SVM classifier had the highest score in portal venous phase. The results were as follows: The AUC value of the poor prognosis group in validation set was 0.865, the sensitivity was 0.77, and the specificity was 0.83. The AUC value of the good prognosis group in validation set was 0.865, the sensitivity was 0.83, and the specificity was 0.77. In arterial phase, the XGBoost classifier had the highest score. The AUC value of the poor prognosis group in validation set was 0.601, the sensitivity was 0.69, and the specificity was 0.38. The AUC value of the good prognosis group in validation set was 0.601, the sensitivity was 0.38, and the specificity was 0.69. The LR classifier had the highest score in delayed phase. The AUC value of poor prognosis group in validation set was 0.628, the sensitivity was 0.62, and the specificity was 0.67. The AUC value of the good prognosis group in validation set was 0.628, the sensitivity was 0.67, and the specificity was 0.62. Conclusion Radiomics analysis represents a promising tool in predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer. The ROI in portal venous phase is most suitable for predicting the efficacy of anti-HER2 therapy, and the SVM algorithm model has the best efficiency.

/pdf/value-of-ct-based-radiomics-in-predicating-the-efficacy-of-1b51gjil.pdf

Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer

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Deep learning-based real-time VPN encrypted traffic identification methods

The systemic immune-inflammation index was non-linear associated with all-cause mortality in individuals with nonalcoholic fatty liver disease.

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements

Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer