IEEE Journal of Biomedical and Health Informatics最新文献_第8页

Continuous Heart Rate Recovery Monitoring with ECG Signals from Wearables: Identifying Risk Groups in the General Population.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-11 DOI: 10.1109/JBHI.2025.3550092

Ayse Dogan, Alka Bishnoi, Richard B Sowers, Manuel E Hernandez

{"title":"Continuous Heart Rate Recovery Monitoring with ECG Signals from Wearables: Identifying Risk Groups in the General Population.","authors":"Ayse Dogan, Alka Bishnoi, Richard B Sowers, Manuel E Hernandez","doi":"10.1109/JBHI.2025.3550092","DOIUrl":"10.1109/JBHI.2025.3550092","url":null,"abstract":"Heart rate recovery (HRR) is a critical indicator of cardiovascular fitness and autonomic nervous system function, reflecting the balance between sympathetic and parasympathetic activity. Slower HRR is often linked to cardiovascular and metabolic disorders, highlighting its potential for identifying high-risk individuals. In this study, we developed a feature engineering approach integrated to wearable device data to classify individuals into high-risk (slower HRR) and low-risk (faster HRR) groups. Data were collected from 38 participants (aged 20 to 76 years, 55.26% women) during treadmill trial, with ECG signals recorded using a smart shirt. Participants with an HRR equal to 28 beats per minute or below were classified as high-risk. Using machine learning classifiers, our approach achieved an area under the curve (AUC) score of 86% with Support Vector Classifier (SVC), demonstrating the feasibility of continuous heart health monitoring via wearable devices. Interestingly, age did not emerge as a significant predictor of HRR in our analysis, possibly due to the impact of lifestyle changes during the lockdown policy of COVID-19 era. This method holds promise for improving cardiovascular health monitoring accessibility and could support physicians in risk assessment and clinical decision-making.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contactless Intelligent Anti-interference Lung Nodule Detection Method for Early Disease Detection.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-11 DOI: 10.1109/JBHI.2025.3550199

Jijing Cai, Lina Wang, Jiuqing Cai, Zixin Deng, Zijia Yang, Hailin Feng

{"title":"Contactless Intelligent Anti-interference Lung Nodule Detection Method for Early Disease Detection.","authors":"Jijing Cai, Lina Wang, Jiuqing Cai, Zixin Deng, Zijia Yang, Hailin Feng","doi":"10.1109/JBHI.2025.3550199","DOIUrl":"10.1109/JBHI.2025.3550199","url":null,"abstract":"Detection of lung nodules is key in the treatment of early-stage lung cancer. Computed tomography (CT) scanning technology is an essential contactless tool. However, stray radiation caused by a patient's slight movements and equipment operation can impair CT images, hindering accurate lung nodule detection. To address these issues, this study proposes an artificial intelligence-based anti-interference lung nodule detection method, which is primarily structured with Yolov8 and combines the modules of adaptive gating sparse attention (AGSA) and haar wavelet downsampling (HWD), referred to as Yolov8-AH. This model aimed to improve the accuracy of lung nodule detection in lung CT images under interference conditions. AGSA focuses on key areas of the image, promoting detection stability even when CT images are disturbed. Furthermore, HWD prioritizes the frequency components corresponding to the size and shape of the nodules, enhancing their visibility for easier detection and analysis. HWD effectively reduces image noise without significantly blurring the lung nodule edges, emphasizing them prominently within the lung tissue. Furthermore, when combined with the Yolov8 deep learning model driven by artificial intelligence, the model could accurately detect lung nodules, significantly aiding in early diagnosis and treatment. The effectiveness of the Yolov8-AH detection model was verified through ablation experiments, experiments under varying noise intensities, and experiments under different noise application ratios. The experimental results demonstrate that, compared to existing lung nodule detection models, the Yolov8-AH model achieves a 24% improvement in mAP50 and an 8.2% improvement in precision.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distinguishing Pathologic Gait in Older Adults Using Instrumented Insoles and Deep Neural Networks.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-10 DOI: 10.1109/JBHI.2025.3549454

Wonhee Lee, Jin Hyun, Seung-Ick Choi, Sangbu Yun, Kwangho Chung, Seok Jong Chung, Jun Kyu Hwang, Eun Joo Yang, Youngjoo Lee, Na Young Kim

{"title":"Distinguishing Pathologic Gait in Older Adults Using Instrumented Insoles and Deep Neural Networks.","authors":"Wonhee Lee, Jin Hyun, Seung-Ick Choi, Sangbu Yun, Kwangho Chung, Seok Jong Chung, Jun Kyu Hwang, Eun Joo Yang, Youngjoo Lee, Na Young Kim","doi":"10.1109/JBHI.2025.3549454","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3549454","url":null,"abstract":"Gait abnormalities are common in the older population owing to aging- and disease-related changes in physical and neurological functions. Differentiating the causes of gait abnormalities is challenging because various abnormal gaits share a similar pattern in older patients. Herein, we propose a deep neural network (DNN) model to classify disease-specific gait patterns in older adults using commercialized instrumented insoles. This study included 150 patients aged ≥ 65 years, divided into the following five groups (N = 30 in each group): healthy older individuals (HI), patients with Parkinson's disease (PD), patients with spastic hemiplegic gait due to stroke (SH), patients with normal-pressure hydrocephalus (NPH), and patients with knee osteoarthritis (OA). Participants performed the timed up and go test (TUGT) wearing the commercialized instrumented insole, GDCA-MD (Gilon, Republic of Korea). Seven data streams were collected from each insole using a 3-axis accelerometer and four pressure sensors and were analyzed. First, the statistical differences among groups in spatiotemporal features during TUGT, such as step count, step length, velocity, acceleration, regularity, and symmetricity, were examined. Second, a two-stage DNN model was developed that distinguishes HI from others in the first network and classifies the pathologic groups in the second network. The areas under the curve were 0.96, 0.88, 0.98, 0.96, and 0.97 for identifying HI, PD, OA, SH, and NPH, respectively. We demonstrated that the proposed DNN model can reliably classify gait abnormalities in an older population using simple instrumented insoles and a test.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smoking Detection and Cessation: An Updated Scoping Review of Digital and Mobile Health Technologies.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-10 DOI: 10.1109/JBHI.2025.3549255

Mirko Casu, Francesco Guarnera, Giusy Rita Maria La Rosa, Sebastiano Battiato, Pasquale Caponnetto, Riccardo Polosa, Rosalia Emma

{"title":"Smoking Detection and Cessation: An Updated Scoping Review of Digital and Mobile Health Technologies.","authors":"Mirko Casu, Francesco Guarnera, Giusy Rita Maria La Rosa, Sebastiano Battiato, Pasquale Caponnetto, Riccardo Polosa, Rosalia Emma","doi":"10.1109/JBHI.2025.3549255","DOIUrl":"10.1109/JBHI.2025.3549255","url":null,"abstract":"Digital and mobile health technologies offer promising solutions for smoking detection and cessation. This scoping review examines the current state of research and development in this field, encompassing smartphone applications, wearable devices, and sensor-based systems. We analyzed 49 studies published between 2019 and 2023 from PubMed and ACM Digital Library, focusing on technology features, outcomes, and evaluation methods. Wearable sensors and smartphone apps show potential in combating smoking addiction and improving quit rates. Motion sensors for hand-to-mouth gesture detection achieve high accuracy in controlled settings but face challenges in real-world applications. Machine learning models and wireless signal detection techniques yield encouraging results but require further refinement. Smartphone apps provide personalized plans and progress tracking, though most rely on manual logging and lack rigorous scientific evaluation. Our findings suggest that digital health technologies could significantly enhance smoking cessation efforts. However, more robust evaluation methods and integration of sensor data with machine learning are needed to improve usability and effectiveness. Continued research and innovation in this field are crucial for developing reliable, practical solutions and integrating these technologies into clinical programs.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

REGEEG: A Regression-Based EEG Signal Processing in Emotion Recognition.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-10 DOI: 10.1109/JBHI.2025.3543729

Oscar Almanza-Conejo, Juan Gabriel Avina-Cervantes, Arturo Garcia-Perez, Mario Alberto Ibarra-Manzano

{"title":"REGEEG: A Regression-Based EEG Signal Processing in Emotion Recognition.","authors":"Oscar Almanza-Conejo, Juan Gabriel Avina-Cervantes, Arturo Garcia-Perez, Mario Alberto Ibarra-Manzano","doi":"10.1109/JBHI.2025.3543729","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3543729","url":null,"abstract":"Electroencephalograms provide a non-invasive and effective method for studying emotion recognition and developing Artificial Intelligence (AI) models to understand human behavior and decision-making processes. This study involved testing several machine learning classification kernels to develop an accurate emotion recognition model capable of classifying emotions stimuli such as \"Boring,\" \"Calm,\" \"Happy,\" and \"Fear\" during gameplay. An emotion classifier was assessed using the publicly available database for an emotion recognition system based on EEG signals and various computer games (GAMEEMO). The signal processing method, referred to as Regression EEG (REGEEG), involves an efficient electrode pairing selector developed for EEG signal processing using a regression algorithm, rotation matrices, director vectors, and robust statistical and polynomial feature extraction. REGEEG and feature extraction methods were evaluated with 28 machine learning kernels, resulting in five kernels with classification performance above 80%, with the K-Nearest Neighbors (k-NN) based model outperforming the rest (achieving over 95% accuracy, F1-Score, and kappa-score). REGEEG performance was further validated using 30 Cross-Validation (CV) folders and 28 in the Leave-one Subject-out (LoSo) technique without impacting the average classification performance. The classification highlights revealed low variance in the CV, while the LoSo approach helped identify outliers in the GAMEEMO dataset. Furthermore, the EEG pair channels selector demonstrates superior performance in classification, indicating a correlation between features and each processed pair of channels.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MMFmiRLocEL: A multi-model fusion and ensemble learning approach for identifying miRNA subcellular localization using RNA structure language model.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3548940

Tao Bai, Junxi Xie, Bin Liu, Yumeng Liu

{"title":"MMFmiRLocEL: A multi-model fusion and ensemble learning approach for identifying miRNA subcellular localization using RNA structure language model.","authors":"Tao Bai, Junxi Xie, Bin Liu, Yumeng Liu","doi":"10.1109/JBHI.2025.3548940","DOIUrl":"10.1109/JBHI.2025.3548940","url":null,"abstract":"MiRNA subcellular localizations (MSLs) are essential for uncovering and understanding miRNA functions in various biological processes. Several computational methods have been proposed for measuring MSL. However, existing methods only rely on manually crafted features based on sequence without considering RNA 3D structure information, and most methods often rely on single-model approaches, which fail to capture the full complexity of biological systems, further hindering predictive accuracy and performance. In this study, we introduce a deep learning-based approach, MMFmiRLocEL, which integrates multi-model fusion and ensemble learning for MSL identification. To the best of our knowledge, MMFmiRLocEL is the first method to combine sequence, structure, and function three information for MSL prediction. Specifically, it employs RNA 3D structure generated by the predicted structural model to construct a structure-based approach for MSL prediction. It also develops a sequence-based prediction method using sequence features and convolutional neural networks, while constructing a function-based prediction method using miRNA-disease association networks and deep residual neural networks. Furthermore, a multi-model fusion approach, employing weighted ensemble strategies, integrates sequence, structure, and function models to enhance the robustness and accuracy of MSL identification. Experimental results demonstrate that MMFmiRLocEL outperforms existing state-of-the-art methods, and then ablation analysis confirmed the significant contribution of the multi-model fusion mechanism to improve the prediction performance.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, Performance Evaluation and Optimization for Intensive Care Networks Based on Non-Hierarchical Overflow Loss Systems.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3549142

Jingjin Wu, Wei Tian, Anqi Wang, Yin-Chi Chan, Eric W M Wong, Kenny Chan, Gavin Joynt

{"title":"Design, Performance Evaluation and Optimization for Intensive Care Networks Based on Non-Hierarchical Overflow Loss Systems.","authors":"Jingjin Wu, Wei Tian, Anqi Wang, Yin-Chi Chan, Eric W M Wong, Kenny Chan, Gavin Joynt","doi":"10.1109/JBHI.2025.3549142","DOIUrl":"10.1109/JBHI.2025.3549142","url":null,"abstract":"In design and optimization of intensive care unit (ICU) networks, one common practice is to prioritize the treatment for patients of higher emergency levels, while ensuring fairness to other patients by guaranteeing a certain Quality of Service (QoS) level. One common approach to realize such priority arrangement is bed reservation policy, which designates a certain number of last occupied beds in each hospital to be exclusively used by certain patient classes. In this paper, we propose an approach that can significantly improve the computational efficiency in obtaining the optimal reservation thresholds for each patient class given their respective requirements, in a non-hierarchical ICU model (where the external emergency patients can possibly be allocated to any ICU hospital) which has been shown to be computationally challenging in performance evaluation and optimization. Specifically, we apply the Information Exchange Surrogate Approximation (IESA) to analytically approximate the key QoS metrics under given reservation thresholds, and the integer Particle Swarm Optimization (PSO) algorithm to search for the optimal threshold based on the approximation results by IESA. We demonstrate numerically, with the real data from ICUs in Hong Kong, that IESA approximation can obtain reasonably accurate results for QoS metrics, and thus lead to accurate optimal reservation thresholds. In addition, our proposed approach combining IESA and PSO can significantly reduce the computation time by more than four orders of magnitude, compared to the state-of-the-art evaluation and optimization methods in existing research for similar problems, especially for ICU networks with practical sizes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Early Parkinson's Disease by Leveraging Speech Foundation Models.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-07 DOI: 10.1109/JBHI.2025.3548917

Quang Dao, Laetitia Jeancolas, Graziella Mangone, Sara Sambin, Alize Chalancon, Manon Gomes, Stephane Lehericy, Jean-Christophe Corvol, Marie Vidailhet, Isabelle Arnulf, Dijana Petrovska Delacretaz, Mounim A El-Yacoubi

{"title":"Detection of Early Parkinson's Disease by Leveraging Speech Foundation Models.","authors":"Quang Dao, Laetitia Jeancolas, Graziella Mangone, Sara Sambin, Alize Chalancon, Manon Gomes, Stephane Lehericy, Jean-Christophe Corvol, Marie Vidailhet, Isabelle Arnulf, Dijana Petrovska Delacretaz, Mounim A El-Yacoubi","doi":"10.1109/JBHI.2025.3548917","DOIUrl":"10.1109/JBHI.2025.3548917","url":null,"abstract":"Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, characterized by a wide range of motor and non-motor symptoms. Among these symptoms, alterations in speech and voice quality stand out as early and prominent indicators of the disease. Recently, the emergence of speech foundation models has revolutionized the field by providing powerful tools for speech processing and feature extraction. In this article, we investigate the capabilities of three state-of the art speech foundation models, wav2vec2.0, Whisper and SeamlessM4T, to develop robust and accurate methods for PD detection from voice recordings. We experiment with both direct feature extraction and finetuning of the foundation models for the PD classification task, and validate the results against clinical and neuroimaging data. We achieve promising results using both pretrained features and models' finetuning, with finetuning providing stronger performance, up to 91.35% for AUC, which is the new state of the art on the ICEBERG dataset. The predictions of our models also show good correlation with clinical as well as DaTSCAN scores, proving the feasibility to apply speech foundation models for detection of early PD.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Arbitrary-Scale Super-Resolution Network for Multi-Contrast MRI With Permuted Cross-Attention.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3548696

Ming Zhao, Jia Fang, Boyang Chen

{"title":"An Arbitrary-Scale Super-Resolution Network for Multi-Contrast MRI With Permuted Cross-Attention.","authors":"Ming Zhao, Jia Fang, Boyang Chen","doi":"10.1109/JBHI.2025.3548696","DOIUrl":"10.1109/JBHI.2025.3548696","url":null,"abstract":"In magnetic resonance imaging (MRI), low-resolution (LR) images often hamper clinical diagnosis and research due to constraints in imaging conditions and technology limitations. Recent studies in super-resolution (SR) reconstruction of multi-contrast MRI have shown promise by leveraging the complementary information from different MRI contrasts. However, existing multi-contrast MRI SR techniques face several challenges: 1) a lack of pre-alignment precision can result in distorted reconstructions; 2) prevailing transformer network structures, with their smaller windows (e.g., 8×8), struggle to effectively capture long-range dependencies and lack the ability to interact between different windows; and 3) current methods are limited to fixed integer scaling (e.g., 2×, 3×, 4×), which limits flexibility and increases complexity in training and storage. To address these challenges, we propose a novel arbitrary-scale SR network for multi-contrast MRI. Specifically, our approach compensates for spatial misalignment between modalities through deformable registration module and employs permuted cross-attention transformer in MR images. In addition, we introduce a ref-scale ensemble implicit attention module that better integrates high-frequency information from reference images and enables arbitrary-scale upsampling. Extensive experiments on two publicly available MRI datasets validate the superiority of our method in multi-contrast MRI SR, demonstrating its significant potential in clinical applications. Our code is available at https://github.com/fangxiaojia0/An-Arbitrary-Scale-Super-Resolution-Network-for-Multi-Contrast-MRI-With-Permuted-Cross-Attention.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook.

IF 6.7 2区医学

IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-03-06 DOI: 10.1109/JBHI.2025.3540507

Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R KhudaBukhsh, Liviu P Dinu

{"title":"On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook.","authors":"Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R KhudaBukhsh, Liviu P Dinu","doi":"10.1109/JBHI.2025.3540507","DOIUrl":"https://doi.org/10.1109/JBHI.2025.3540507","url":null,"abstract":"Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"PP ","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0