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Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning

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DIGITAL HEALTH 2024年 10卷

作者： Choi, Hyoungshin Cho, Yesol Min, Choongki Kim, Kyungnam Kim, Eunji Lee, Seungmin Kim, Jae-Jin Waycen Inc AI Medtech R&D Seoul South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Inst Basic Sci Ctr Neurosci Imaging Res Suwon South Korea Yonsei Univ Inst Behav Sci Med Coll Med Seoul South Korea Yonsei Univ Gangnam Severance Hosp Coll Med Dept Psychiat 211 Eonju-ro Seoul 06273 South Korea

Objective Social anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom *** We collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive *** Classifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84-90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of *** Leveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.

关键词： Digital phenotype social anxiety disorder autoencoder representation learning integrated gradient multiclassification

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Road Surface Damage Detection Using Fully Convolutional Neural Networks and Semi-Supervised Learning

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SENSORS 2019年第24期19卷 5501页

作者： Chun, Chanjun Ryu, Seung-Ki Korea Inst Civil Engn & Bldg Technol KICT Future Infrastruct Res Ctr Goyang 10223 South Korea

The various defects that occur on asphalt pavement are a direct cause car accidents, and countermeasures are required because they cause significantly dangerous situations. In this paper, we propose fully convolutional neural networks (CNN)-based road surface damage detection with semi-supervised learning. First, the training DB is collected through the camera installed in the vehicle while driving on the road. Moreover, the CNN model is trained in the form of a semantic segmentation using the deep convolutional autoencoder. Here, we augmented the training dataset depending on brightness, and finally generated a total of 40,536 training images. Furthermore, the CNN model is updated by using the pseudo-labeled images from the semi-supervised learning methods for improving the performance of road surface damage detection technique. To demonstrate the effectiveness of the proposed method, 450 evaluation datasets were created to verify the performance of the proposed road surface damage detection, and four experts evaluated each image. As a result, it is confirmed that the proposed method can properly segment the road surface damages.

关键词： road surface damage semantic segmentation autoencoder convolutional neural network semi-supervised learning

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The application of unsupervised deep learning in predictive models using electronic health records

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BMC MEDICAL RESEARCH METHODOLOGY 2020年第1期20卷 37-37页

作者： Wang, Lei Tong, Liping Davis, Darcy Arnold, Tim Esposito, Tina Renmin Univ China Sch Stat 59 Zhong Guan Cun Ave Beijing Peoples R China Univ Illinois Dept Math Stat & Comp Sci 851 S Morgan St Chicago IL 60607 USA Advocate Aurora Hlth 3075 Highland Pkwy Downers Grove IL 60515 USA Cerner Corp 2800 Rockcreek Pkwy North Kansas City MO 64117 USA

Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks. Methods We compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals. Results On simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper. Conclusions We conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and

关键词： autoencoder LASSO Enhanced Reg Predictive model Predictive performance Important response-specific predictors

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Dimensionality reduction for protein secondary structure and solvent accesibility prediction

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2018年第5期16卷 1850020页

作者： Aydin, Zafer Kaynar, Oguz Gormez, Yasin Abdullah Gul Univ Dept Comp Engn TR-38080 Kayseri Turkey Cumhuriyet Univ Dept Management Informat Syst TR-58000 Sivas Turkey

Secondary structure and solvent accessibility prediction provide valuable information for estimating the three dimensional structure of a protein. As new feature extraction methods are developed the dimensionality of the input feature space increases steadily. Reducing the number of dimensions provides several advantages such as faster model training, faster prediction and noise elimination. In this work, several dimensionality reduction techniques have been employed including various feature selection methods, autoencoders and PCA for protein secondary structure and solvent accessibility prediction. The reduced feature set is used to train a support vector machine at the second stage of a hybrid classifier. Cross-validation experiments on two difficult benchmarks demonstrate that the dimension of the input space can be reduced substantially while maintaining the prediction accuracy. This will enable the incorporation of additional informative features derived for predicting the structural properties of proteins without reducing the accuracy due to overfitting.

关键词： Secondary structure prediction solvent accessibility prediction feature selection dimension reduction autoencoder

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ML-SDNIDS: an attack detection mechanism for SDN based on machine learning

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INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY 2022年第1-2期19卷 118-141页

作者： Guo, Xian Bai, Wei Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China

With the rapid development of network technology, there are more and more application scenarios of software defined networking (SDN), such as big data, cloud computing, internet of things, etc. However, the facilities in the SDN network face security issues such as DDoS attacks, network monitoring, and privacy. In addition, the SDN controller is also the main target of the attacker. This paper makes a simple analysis of the security risks in SDN and proposes a machine learning-based intrusion detection system for SDN (ML-SDNIDS). According to the characteristics of SDN, ML-SDNIDS uses autoencoder and one-class support vector machine algorithm to train intrusion detection model in the control plane, and uses P4 programming language combined with machine learning algorithm to realise real-time intrusion detection function in the data plane. And compared with the traditional SVM and OCSVM intrusion detection models in the latest intrusion detection dataset CIC-DDoS2019, the experimental results show that the scheme proposed in this paper has greatly improved the detection accuracy and the execution efficiency of the model. In addition, this experimental scheme can make the intrusion detection accuracy of data plane P4 switch as high as 97%, and its packet transmission efficiency is still millisecond.

关键词： software defined networking SDN autoencoder one-class support vector machine OCSVM intrusion detection system IDS P4

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A Composite Noise Removal Network Based on Multi-domain Adaptation

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 1194-1205页

作者： Bai, Fan Li, Pengfei Sun, Haoyang Zhang, Hui Shenyang Ligong Univ Sch Equipment Engn Shenyang Peoples R China Beijing Inst Technol Sch Mech & Elect Engn Sci & Technol Electromech Dynam Control Lab Beijing Peoples R China Beijing Inst Technol Beijing Sch Mech & Elect Engn Beijing Peoples R China

the limitation of conventional singlescene image denoising algorithms in filtering mixed environmental disturbances, and recognizing the drawbacks of cascaded image enhancement algorithms, which have poor realtime performance and high computational demands, The composite weather adaptive denoising network (CWADN) is proposed. A Cascade Hourglass Feature Extraction Network is constructed with a visual attention mechanism to extract characteristics of rain, fog, and low-light noise from authentic natural images. These features are then transferred from their original real distribution domain to a synthetic distribution domain using a deep residual convolutional neural network. The generator and style encoder of the adversarial network work together to adaptively remove the transferred noise through a combination of supervised and unsupervised training, this approach achieves adaptive denoising capabilities tailored to complex natural environmental noise. Experimental results demonstrate that the proposed denoising network yields a high signal-to-noise ratio while maintaining excellent image fidelity. It effectively prevents image distortion, particularly in critical target areas. Additionally, it adapts to various types of mixed noise, making it a valuable tool for preprocessing images in advanced machine vision algorithms such as target recognition and tracking.

关键词： Image denoising domain adaptation generative adversarial network autoencoder

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Portfolio Learning Based on Deep Learning

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FUTURE INTERNET 2020年第11期12卷 202页

作者： Pan, Wei Li, Jide Li, Xiaoqiang Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China's stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market's leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

关键词： quantitative trading portfolio autoencoder convolutional natural network (CNN) clustering

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Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values

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EBIOMEDICINE 2020年 62卷 103081-103081页

作者： Zhang, Zhongheng Pan, Qing Ge, Huiqing Xing, Lifeng Hong, Yucai Chen, Pengpeng Zhejiang Univ Sch Med Sir Run Run Shaw Hosp Dept Emergency Med Hangzhou 310016 Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Univ Sch Med Sir Run Shaw Hosp Dept Resp Care Hangzhou Peoples R China

Background: Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets. Methods: The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included. autoencoder was used to extract representative features for k-means clustering. Genetic algorithms (GA) were employed to derive a parsimonious 5-gene class prediction model. The class model was then applied to external datasets (n = 780) to evaluate its prognostic and predictive performance. Findings: A total of 12 datasets involving 1613 patients were included. Two classes were identified in the discovery cohort (n = 685). Class 1 was characterized by immunosuppression with higher mortality than class 2 (21.8% [70/321] vs. 12.1% [44/364];p < 0.01 for Chi-square test). A 5-gene class model (C14orf159, AKNA, PILRA, STOM and USP4) was developed with GA. In external validation cohorts, the 5-gene class model (AUC: 0.707;95% CI: 0.664 - 0.750) performed better in predicting mortality than sepsis response signature (SRS) endotypes (AUC: 0.610;95% CI: 0.521 - 0.700), and performed equivalently to the APACHE II score (AUC: 0.681;95% CI: 0.595 - 0.767). In the dataset E-MTAB-7581, the use of hydrocortisone was associated with increased risk of mortality (OR: 3.15 [1.13, 8.82];p = 0.029) in class 2. The effect was not statistically significant in class 1 (OR: 1.88 [0.70, 5.09];p = 0.211). Interpretati

关键词： Sepsis Endotype Transcriptome autoencoder

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A Lightweight Method of Myocardial Infarction Detection and Localization From Single Lead ECG Features Using Machine Learning Approach

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2024年第4期8卷 1页

作者： Anwar, Sk. Md. Shafique Pal, Debasmita Mukhopadhyay, Sumitra Gupta, Rajarshi Univ Calcutta Inst Radio Phys & Elect Kolkata 700009 India Univ Calcutta Dept Appl Phys Kolkata 700009 India

Computerized myocardial infarction (MI) detection and localization can be useful for early prevention of its aggravation and related cardiac health complications. However, the published research either focuses on binary classification or implements complex classifiers for localization to achieve good accuracy. In this letter, the objective is to implement an 11-class MI localization system on resource-constrained hardware with low complexity and latency. A simple and optimized autoencoder-k-NN classifier has been used to achieve accuracy and F1-score of 99.74% and 99.20%, respectively, while evaluating single lead Electrocardiogram (ECG) features from the PTB-Diagnostic ECG database. A standalone hardware implementation with an ARM-v6-based controller resulted in a latency and runtime memory engagement of 0.48 s and 4.31 MB, respectively, to process 5 s ECG data. The present research can be useful for quick screening of MI for portable healthcare applications.

关键词： Sensor signal processing autoencoder classifier Electrocardiogram (ECG) localization myocardial infarction (MI)

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On the benefits of self-taught learning for brain decoding

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GIGASCIENCE 2023年第12期12卷 giad029页

作者： Germani, Elodie Fromont, Elisa Maumet, Camille Univ Rennes Inria CNRS InsermIRISA UMR 6074Empenn ERL U 1228 F-35000 Rennes France Univ Rennes IUF CNRS IRISA UMR 6074Inria F-35000 Rennes France

Context: We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. Results: We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. Conclusion: The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.

关键词： self-taught learning brain decoding autoencoder convolutional neural network deep learning

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