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Efficacy of using a digital health intervention model using community health workers for primary health services in Bangladesh: a repeated cross-sectional observational study

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BMC public health 2025年第1期25卷 1833页

作者： Marzia Zaman Rubaiyat Alim Hridhee Refat Ahmed Bhuiyan Charles Aunkan Gomes Md Mashiar Rahman Sheikh Mohammed Shariful Islam Farhana Sarker Khondaker A Mamun CMED Health Limited Dhaka Bangladesh. AIMS Lab Institute of Research Innovation Incubation and Commercialization (IRIIC) United International University Dhaka Bangladesh. Department of Computer Science and Engineering United International University Dhaka Bangladesh. Palli Karma-Sahayak Foundation Dhaka Bangladesh. Institute for Physical Activity and Nutrition Faculty of Health Deakin University Melbourne Australia. Center for Computational & Data Sciences (CCDS) and Department of Computer Science and Engineering Independent University Dhaka Bangladesh. AIMS Lab Institute of Research Innovation Incubation and Commercialization (IRIIC) United International University Dhaka Bangladesh. mamun@cse.uiu.ac.bd. Department of Computer Science and Engineering United International University Dhaka Bangladesh. mamun@cse.uiu.ac.bd.

OBJECTIVE:This study aims to evaluate the effectiveness of a digital health intervention model by observing people's health conditions in a rural area of Bangladesh. Through a repeated cross-sectional design, health outcomes were assessed at six-month intervals over 18 months. METHODS:The data presented in this paper are obtained from the model's pilot implementation in one catchment area in Bangladesh. The model involved community health workers (CHWs) using a smart health kit and Artificial Intelligence(ai)-based mobile application to provide monthly doorstep health education, screenings, risk assessments, and digital referrals. Socio-demographic and health measurement data are presented as proportions with 95% confidence intervals (CIs). Multivariate logistic regression was used to analyze the association between diseases and their respective risk factors. We compared health vitals across three consecutive periods to determine the effectiveness of the model in improving health outcomes over time. RESULTS:The model served 32,581 people from 7,090 households during this operation. We found that 21.76% of the served population were overweight, 8.18% had prehypertension, 16.45% had high blood glucose, and 11% children were malnourished. From the analysis of risk factors, we found that people aged were associated with developing hypertension, diabetes, and cardiovascular diseases(CVD). CVD was associated with hypertension and stroke. A comparative analysis of different periods showed an improvement in BMI, BP, and MUAC. Blood glucose measurements did not show significant improvement. CONCLUSION:This study highlights the feasibility and effectiveness of digital health interventions in enhancing primary healthcare in rural settings. Integration of ai-driven decision support with CHW-led health education and screenings can improve early detection, intervention and management of non-communicable diseases (NCDs). Scaling up this model through public-private partnerships o

关键词： Artificial intelligence Clinical decision support system (CDSS) Community health workers (CHWs) Digital health inclusion Digital health intervention model Health equity Non-communicable diseases (NCD) Primary healthcare Sustainable development goals Universal health coverage

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Biomedical data and ai

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(ai) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new ai based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things

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Scientific Reports 2025年第1期15卷 1-27页

作者： Singh, Shailendra Pratap Kumar, Gyanendra Ahirwar, Umakant Selvarajan, Shitharth Khan, Firoz Department of Computer Science and Engineering Madan Mohan Malaviya University of Technology Gorakhpur-273010 (U.P.) UP Gorakhpur India Department of IoT and IS Manipal University Jaipur Jaipur India Computer Engineering and Application GLA University UP Mathura India Department of Computer Science Kebri Dehar University Kebri Dehar 3060 Ethiopia Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Center for Information and Communication Sciences Ball State University Muncie United States

In the context of Internet of Things (IoT), optimizing quality of service (QoS) parameters is a critical challenge due to its heterogeneous and resource-constrained nature. This paper proposes a novel quantum-inspired multi-objective optimization algorithm for IoT service management. Traditional multi-objective optimization algorithms often face limitations such as slow convergence and susceptibility to local optima, reducing their effectiveness in complex IoT environments. To address these issues, we introduce a quantum-inspired hybrid algorithm that combines the strengths of Multi-Objective Grey Wolf Optimization Algorithm (MOGWOA) and Multi-Objective Whale Optimization Algorithm (MOWOA), enhanced with quantum principles. This novel integration overcomes the limitations of traditional algorithms by improving convergence speed and avoiding local optima. The hybrid algorithm enhances QoS in IoT applications by achieving superior optimization in terms of energy efficiency, latency reduction, convergence, and coverage cost. The incorporation of quantum-inspired mechanisms, such as quantum position and behavior, strengthens the exploration and exploitation capabilities of the algorithm, enabling faster and more accurate optimization. Extensive simulations and testing demonstrate the proposed method’s superior performance compared to existing algorithms, validating its effectiveness in addressing key IoT challenges. © The Author(s) 2025.

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

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IEEE Wireless Communications Letters 2025年

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： aircraft communication

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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Enhancing lung cancer detection through integrated deep learning and transformer models

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Scientific Reports 2025年第1期15卷 1-28页

作者： Durgam, Revathi Panduri, Bharathi Balaji, V. Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Data Science AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad India Department of CSE-AIML Vardhaman College of Engineering Hyderabad India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering Kebri Dehar University Kebri Dehar Ethiopia Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question;this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches. © The Author(s) 2025.

关键词： And classification Deep learning Disease detection Lung Cancer Optimization Segmentation Transformer models

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Performance of single-agent and multi-agent language models in Spanish language medical competency exams

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BMC Medical Education 2025年第1期25卷 1-11页

作者： Altermatt, Fernando R. Neyem, Andres Sumonte, Nicolas Mendoza, Marcelo Villagran, Ignacio Lacassie, Hector J. Division of Anesthesiology School of Medicine Pontificia Universidad Católica de Chile Marcoleta 377 RM Santiago 8320000 Chile Department of Computer Science School of Engineering Pontificia Universidad Católica de Chile Vicuña Mackenna 6840 RM Santiago 7820436 Chile Department of Kinesiology Health Sciences School Pontificia Universidad Católica de Chile Vicuña Mackenna 6840 RM Santiago 7820436 Chile National Center for Artificial Intelligence (CENIA) National Research and Development Agency (ANID) Vicuña Mackenna 6840 RM Santiago 7820436 Chile Millennium Institute for Foundational Research On Data (IMFD) Pontificia Universidad Católica de Chile Vicuña Mackenna 6840 RM Santiago 7820436 Chile

Background: Large language models (LLMs) like GPT-4o have shown promise in advancing medical decision-making and education. However, their performance in Spanish-language medical contexts remains underexplored. This study evaluates the effectiveness of single-agent and multi-agent strategies in answering questions from the EUNACOM, a standardized medical licensure exam in Chile, across 21 medical specialties. Methods: GPT-4o was tested on 1,062 multiple-choice questions from publicly available EUNACOM preparation materials. Single-agent strategies included Zero-Shot, Few-Shot, Chain-of-Thought (CoT), Self-Reflection, and MED-PROMPT, while multi-agent strategies involved Voting, Weighted Voting, Borda Count, MEDAGENTS, and MDAGENTS. Each strategy was tested under three temperature settings (0.3, 0.6, 1.2). Performance was assessed by accuracy, and statistical analyses, including Kruskal–Wallis and Mann–Whitney U tests, were performed. Computational resource utilization, such as API calls and execution time, was also analyzed. Results: MDAGENTS achieved the highest accuracy with a mean score of 89.97% (SD = 0.56%), outperforming all other strategies (p < 0.001). MEDAGENTS followed with a mean score of 87.99% (SD = 0.49%), and the CoT with Few-Shot strategy scored 87.67% (SD = 0.12%). Temperature settings did not significantly affect performance (F2,54 = 1.45, p = 0.24). Specialty-level analysis showed the highest accuracies in Psychiatry (95.51%), Neurology (95.49%), and Surgery (95.38%), while lower accuracies were observed in Neonatology (77.54%), Otolaryngology (76.64%), and Urology/Nephrology (76.59%). Notably, several exam questions were correctly answered using simpler single-agent strategies without employing complex reasoning or collaboration frameworks. Conclusions and relevance: Multi-agent strategies, particularly MDAGENTS, significantly enhance GPT-4o’s performance on Spanish-language medical exams, leveraging collaboration to improve diagnostic accuracy.

关键词： GPT-4o Large language models Medical ai Medical decision-making Spanish medical contexts

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Learning Counterfactual Outcomes Under Rank Preservation

arXiv

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arXiv 2025年

作者： Wu, Peng Li, Haoxuan Zheng, Chunyuan Zeng, Yan Chen, Jiawei Liu, Yang Guo, Ruocheng Zhang, Kun School of Mathematics and Statistics Beijing Technology and Business University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China College of Computer Science and Technology Zhejiang University China Computer Science and Engineering University of California Santa Cruz Bolivia ByteDance Research China Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applications in fields such as epidemiology, econometrics, and management science. Previous methods rely on a known structural causal model (SCM) or assume the homogeneity of the exogenous variable and strict monotonicity between the outcome and exogenous variable. In this paper, we propose a principled approach for identifying and estimating the counterfactual outcome. We first introduce a simple and intuitive rank preservation assumption to identify the counterfactual outcome without relying on a known structural causal model. Building on this, we propose a novel ideal loss for theoretically unbiased learning of the counterfactual outcome and further develop a kernel-based estimator for its empirical estimation. Our theoretical analysis shows that the rank preservation assumption is not stronger than the homogeneity and strict monotonicity assumptions, and shows that the proposed ideal loss is convex, and the proposed estimator is unbiased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Privacy-Preserving Decentralized Signature-Based Access Control

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Han, Jinguang Chen, Liqun Susilo, Willy Southeast University School of Cyber Science and Engineering Jiangsu Nanjing210096 China Ministry of Education Engineering Research Center of Blockchain Application China University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Wollongong School of Computing & Information Technology New South Walse WollongongNSW2522 Australia

Decentralized access control reduces trust on the central authority. Considering privacy issues, privacy-preserving decentralized access control (PPDAC) schemes were proposed. However, existing schemes did not consider the unforgeability and aggregtability of access permissions and the traceability of illegal users. In this paper, we propose a privacy-preserving decentralized signature-based access control (PPDSBAC) scheme to fix the above problems. In our scheme, each access permission is a multi- signatures on a keyword, time period and pseudonym, hence is unforgeable and aggregatable. Services are encrypted by using different keywords and time periods. A user can use a pseudonym to access a service directly if he/she gets required access permissions from required authorization authorities. Furthermore, in the case that trace is required, a trace authority can de-anonymise users and identify their real identities. We formalize both the definition and security model of our scheme, and propose a concrete construction. Then, the implementation and evaluation are conducted to show its efficiency. Finally, the security of our scheme is formally proven. The novelty is to construct a PPDAC scheme with unlinkable access requests, unforgeable and aggregatable access permissions, non-interactive authentication and traceability. © 2004-2012 IEEE.

关键词： Authentication

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