Ji-eun Yu¹, Eunji Ahn², Hanbit Jin², Dongsu Kim^2,*

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1353-1367, 2025, DOI:10.32604/ijmhp.2025.067407 - 30 September 2025

Abstract Background: Patients with cancer are confronted not only with physical changes and pain but also with significant psychological challenges, including distress, anxiety, and depression, as a consequence of their diagnosis and treatment. This study aimed to identify the factors influencing anxiety or depression in patients with cancer, examine the relationship between the duration since cancer diagnosis and psychological state, and explore the association between these factors and the use of Korean medicine (KM). Methods: This study utilized data from the 2018 Korea Health Panel spanning 2008 to 2018. The analysis focused on adult participants (aged… More >

Beth Steinberg^1,*, Yulia Mulugeta¹, Catherine Quatman-Yates², Maeghan Williams², Anvitha Gogineni¹, Maryanna Klatt¹

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1237-1264, 2025, DOI:10.32604/ijmhp.2025.067232 - 30 September 2025

Abstract Background: Community-based first responders face high levels of workplace stressors that can profoundly impact their physical and mental health. Mindfulness-based interventions have shown promise in decreasing stress and increasing psychological resilience; however, implementation is difficult due to unpredictability of the job, department culture, and generational preferences. The objective of this qualitative study was to identify and enhance understanding of the specific needs and potential barriers and facilitators for the implementation of mindfulness-based programming for community-based first responders. Methods: A phenomenological qualitative study design was used to gain insights into the lived experiences of first responders… More >

Traci N. Adams^1,#,*, Haley Belt^1,#, E. Whitney Pollio², Leah Cohen¹, Roma M. Mehta¹, Hetal J. Patel¹, Rosechelle M. Ruggiero¹, Carol S. North³

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1369-1382, 2025, DOI:10.32604/ijmhp.2025.066495 - 30 September 2025

Abstract Background: While quantitative research has determined that emotional distress and psychiatric illness among frontline healthcare workers increased with the COVID-19 pandemic, detailed qualitative data describing their personal experiences are needed in order to make appropriate plans to address provider mental health in future pandemics. This study aims to further explore the psychological effects of the pandemic on COVID-19 ICU clinicians and administrators through focus groups. Methods: Two separate 2-h focus groups of physicians were conducted, one with frontline faculty clinicians and another with administrators. Qualitative data analysis was conducted. Results: In September and November 2023, volunteer… More >

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Florentin Smarandache¹, Saleh I. Alzahrani², Sulaiman Al Amro³, Ijaz Ahmad⁴, Mubashir Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3715-3735, 2025, DOI:10.32604/cmes.2025.068736 - 30 September 2025

Abstract Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight… More >

Panyu Liu¹, Tongqing Zhou^1,*, Guofeng Lu², Huaizhe Zhou³, Zhiping Cai¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2913-2934, 2025, DOI:10.32604/cmc.2025.069175 - 23 September 2025

Abstract The isolation of healthcare data among worldwide hospitals and institutes forms barriers for fully realizing the data-hungry artificial intelligence (AI) models promises in renewing medical services. To overcome this, privacy-preserving distributed learning frameworks, represented by swarm learning and federated learning, have been investigated recently with the sensitive healthcare data retaining in its local premises. However, existing frameworks use a one-size-fits-all mode that tunes one model for all healthcare situations, which could hardly fit the usually diverse disease prediction in practice. This work introduces the idea of ensemble learning into privacy-preserving distributed learning and presents the More >

Fatma S. Alrayes¹, Mohammed Zakariah^2,*, Mohammed K. Alzaylaee³, Syed Umar Amin⁴, Zafar Iqbal Khan⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3457-3484, 2025, DOI:10.32604/cmc.2025.068599 - 23 September 2025

Abstract Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that… More >

Parul Dubey^1,*, Pushkar Dubey², Mohammed Zakariah^3,4,*, Abdulaziz S. Almazyad⁴, Deema Mohammed Alsekait⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3897-3919, 2025, DOI:10.32604/cmc.2025.066882 - 23 September 2025

Abstract Psychological distress detection plays a critical role in modern healthcare, especially in ambient environments where continuous monitoring is essential for timely intervention. Advances in sensor technology and artificial intelligence (AI) have enabled the development of systems capable of mental health monitoring using multi-modal data. However, existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings. This paper addresses these challenges by proposing TRANS-HEALTH, a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention (BDI) reasoning for real-time psychological distress detection. The framework utilizes a multimodal dataset containing EEG, GSR, heart rate, and activity… More >

Enas W. Abood¹, Ali A. Yassin^2,*, Zaid Ameen Abduljabbar^2,3,4,*, Vincent Omollo Nyangaresi^5,6, Iman Qays Abduljaleel², Abdulla J. Y. Aldarwish², Husam A. Neamah^7,8

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2305-2345, 2025, DOI:10.32604/cmc.2025.066579 - 23 September 2025

Abstract The digital revolution era has impacted various domains, including healthcare, where digital technology enables access to and control of medical information, remote patient monitoring, and enhanced clinical support based on the Internet of Health Things (IoHTs). However, data privacy and security, data management, and scalability present challenges to widespread adoption. This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT, highlighting their critical roles in ensuring secure data exchange and patient privacy. This includes various authentication technologies and strategies, such as biometric and multi-factor authentication, as well as the influence More >

You-Jyun Yeh¹, Edward T.-H. Chu^1,*, Chia-Rong Lee², Jiun Hsu³, Hui-Mei Wu³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3369-3391, 2025, DOI:10.32604/cmc.2025.065336 - 23 September 2025

Abstract Porters play a crucial role in hospitals because they ensure the efficient transportation of patients, medical equipment, and vital documents. Despite its importance, there is a lack of research addressing the prediction of completion times for porter tasks. To address this gap, we utilized real-world porter delivery data from National Taiwan University Hospital, Yunlin Branch, Taiwan. We first identified key features that can influence the duration of porter tasks. We then employed three widely-used machine learning algorithms: decision tree, random forest, and gradient boosting. To leverage the strengths of each algorithm, we finally adopted an… More >