Journal of Medical Systems最新文献

{"title":"Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data.","authors":"Inyong Jeong, Seokjin Kong, Yeongmin Kim, Yihyun Kim, Byeongsu Kim, Se-Jin Ahn, Ju-Wan Kim, Hwamin Lee","doi":"10.1007/s10916-025-02180-5","DOIUrl":"https://doi.org/10.1007/s10916-025-02180-5","url":null,"abstract":"<p><p>The increasing availability of wearable device data provides an opportunity for developing personalized models for health monitoring and condition prediction. Unlike conventional approaches that rely on pooled data from diverse individuals, our study explores the strategy of intentionally overfitting models to personal data and subsequently applying a transfer learning technique to refine performance for each user. We predicted Next-Day Condition (NDC) and Next-Day Emotion (NDC) while considering diverse features such as physical activity, sleep patterns, environmental context, and self-reported measures. Initial experiments showed that models trained at the sample level performed better on evaluation data but failed to generalize effectively during external validation. In contrast, our personalized learning approach, initiated with a pre-trained model, significantly enhanced accuracy within ten days of incremental user-specific training. Although generalization across the entire cohort diminished after individual tailoring, extended individualized training increased the overall predictive accuracy for each participant's personal data. The interpretation of feature importance using Shapley's additive explanations revealed substantial variability in the features influencing predictions across individuals, emphasizing the need for tailored health models. These findings highlight the potential of combining intentional overfitting and transfer learning in constructing high-performance user-specific predictive models from wearable data. Future research should expand the number of participants, extend the training period, and refine these methods to bolster personalized digital health solutions.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"45"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Japan-Specific HL7 FHIR Medication-Related Profiles.","authors":"Shinji Kobayashi, Masahiko Kimura, Yoshinori Kodama, Atsushi Takada, Satomi Nagashima, Yoshimasa Kawazoe, Kazuhiko Ohe","doi":"10.1007/s10916-025-02173-4","DOIUrl":"https://doi.org/10.1007/s10916-025-02173-4","url":null,"abstract":"<p><p>Adoption of the HL7 Fast Healthcare Interoperability Resources (FHIR) as the global standard for healthcare information exchange has encouraged many countries to develop localized implementation guides that align with their specific regulatory and clinical needs. In 2018, the NeXEHRS Academic Research Group of the Japan Association of Medical Informatics (JAMI) commenced the creation of JP Core, a collection of FHIR profiles tailored for the Japanese healthcare environment. This study is focused on JP Core v1.2.0 released in December 2024. This includes eight medication-related profiles and 23 extensions that optimize Japanese prescription workflows by incorporating local terminologies such as the HOT and YJ Codes. The transition from Simplifier.net to GitHub, coupled with adoption of the Sushi framework, improved collaboration, version control, and standardization.We also examine the Japanese approach to FHIR governance, highlighting the need for a formal regulatory framework akin to the US Core Implementation Guide and the European governance models. Key challenges include terminology binding, cross-border ePrescription integration, and ongoing profile maintenance. Recommendations include the establishment of a national governance body, alignment of domestic terminologies with international standards (e.g., SNOMED CT), and alignment of JP Core interoperability with global frameworks such as the International Patient Summary (IPS) and the European Union (EU) eHealth guidelines. By analyzing the evolution of JP Core and the integration thereof into the Japanese ePrescription ecosystem, this paper provides insights into future FHIR implementation in Japan and highlights lessons learned from international governance structures.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"46"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Toolkit for Classifying Digital Health Interventions, Services, and Applications Based on the WHO Framework.","authors":"Anuradha Liyanage, Daniela Wurhofer, Mahdi Sareban, Gunnar Treff, Josef Niebauer, Rada Hussein","doi":"10.1007/s10916-025-02172-5","DOIUrl":"10.1007/s10916-025-02172-5","url":null,"abstract":"<p><p>The rapidly advancing digital health requires a standardized approach to classifying Digital Health Interventions (DHIs) for better planning, monitoring, and resource distribution. The World Health Organisation (WHO) developed a Classification for Digital Health Interventions, Services, and Applications in Health (CDISAH) in response to this need. The purpose of this study was to develop an interactive toolkit based on WHO's CDISAH to enhance categorization, making it more interactive, user-friendly, and effective in classifying DHI services and applications, and demonstrate its practical implementation in the field of cardiac rehabilitation. We used a descriptive approach with a seven-step iterative process to create the toolkit. The process began with a review of best practices for converting framework into toolkit, followed by drafting an initial toolkit structure, which was refined through team discussions. The content was based on WHO CDISAH. Expert feedback was incorporated, and quality assurance was conducted through internal and external reviews. The toolkit's functionality and usability were evaluated through a use case including DHIs, services, and applications for cardiac rehabilitation. The toolkit for WHO CDISAH has a structured interface with clear definitions, practical examples, and intuitive navigation across three main axes: health system challenges, digital health interventions, and digital health applications and services. Pilot testing improved its usability and functionality for accurate classification, highlighting areas for refinement and identifying challenges and solutions for practical implementation. The developed toolkit provides a standardised, portable platform for classifying the multimodal DHIs that align with the framework presented by WHO.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"44"},"PeriodicalIF":3.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Chatbots as Sources of STD Information: A Study on Reliability and Readability.","authors":"Hüseyin Alperen Yıldız, Emrullah Söğütdelen","doi":"10.1007/s10916-025-02178-z","DOIUrl":"10.1007/s10916-025-02178-z","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) chatbots are increasingly used for medical inquiries, including sensitive topics like sexually transmitted diseases (STDs). However, concerns remain regarding the reliability and readability of the information they provide. This study aimed to assess the reliability and readability of AI chatbots in providing information on STDs. The key objectives were to determine (1) the reliability of STD-related information provided by AI chatbots, and (2) whether the readability of this information meets the recommended standarts for patient education materials.</p><p><strong>Methods: </strong>Eleven relevant STD-related search queries were identified using Google Trends and entered into four AI chatbots: ChatGPT, Gemini, Perplexity, and Copilot. The reliability of the responses was evaluated using established tools, including DISCERN, EQIP, JAMA, and GQS. Readability was assessed using six widely recognized metrics, such as the Flesch-Kincaid Grade Level and the Gunning Fog Index. The performance of chatbots was statistically compared in terms of reliability and readability.</p><p><strong>Results: </strong>The analysis revealed significant differences in reliability across the AI chatbots. Perplexity and Copilot consistently outperformed ChatGPT and Gemini in DISCERN and EQIP scores, suggesting that these two chatbots provided more reliable information. However, results showed that none of the chatbots achieved the 6th-grade readability standard. All the chatbots generated information that was too complex for the general public, especially for individuals with lower health literacy levels.</p><p><strong>Conclusion: </strong>While Perplexity and Copilot showed better reliability in providing STD-related information, none of the chatbots met the recommended readability benchmarks. These findings highlight the need for future improvements in both the accuracy and accessibility of AI-generated health information, ensuring it can be easily understood by a broader audience.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"43"},"PeriodicalIF":3.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11968469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Health Transformation in Ecuador: Progress, Barriers, and Future Directions.","authors":"Jorge Vasconez-Gonzalez, Harold Alexander-León, Esteban Ortiz-Prado","doi":"10.1007/s10916-025-02174-3","DOIUrl":"https://doi.org/10.1007/s10916-025-02174-3","url":null,"abstract":"<p><p>Digital health represents a tool that offers numerous advantages and facilitates various aspects of healthcare. Despite the considerable development and implementation of digital health in first-world countries, including the creation of various policies, third-world countries face a series of challenges in its implementation and development. These challenges include a lack of both economic and technological resources, as well as a shortage of policies related to its development and implementation. This article describes the approaches to digital health transformation in Ecuador, to what extent digital health has been developed in the country, the barriers present, and the benefits of its implementation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"41"},"PeriodicalIF":3.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper-DREAM, a Multimodal Digital Transformation Hypertension Management Platform Integrating Large Language Model and Digital Phenotyping: Multicenter Development and Initial Validation Study.","authors":"Yijun Wang, Tongjian Zhu, Tong Zhou, Bing Wu, Wuping Tan, Kezhong Ma, Zhuoya Yao, Jian Wang, Siyang Li, Fanglin Qin, Yannan Xu, Liguo Tan, Jinjun Liu, Jun Wang","doi":"10.1007/s10916-025-02176-1","DOIUrl":"https://doi.org/10.1007/s10916-025-02176-1","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Within the mHealth framework, systematic research that collects and analyzes patient data to establish comprehensive digital health archives for hypertensive patients, and leverages large language models (LLMs) to assist clinicians in health management and Blood Pressure (BP) control remains limited. In this study, our aims to describe the design, development and usability evaluation process of a management platform (Hyper-DREAM) for hypertension. Our multidisciplinary team employed an iterative design approach over the course of a year to develop the Hyper-DREAM platform. This platform's primary functionalities encompass multimodal data collection (personal hypertensive digital phenotype archive), multimodal interventions (BP measurement, medication assistance, behavior modification, and hypertension education) and multimodal interactions (clinician-patient engagement and BP Coach component). In August 2024, the mHealth App Usability Questionnaire (MAUQ) was conducted involving 51 hypertensive patients recruited from three distinct centers. In parallel, six clinicians engaged in management activities and contributed feedback via the Doctor's Software Satisfaction Questionnaire (DSSQ). Concurrently, a real-world comparative experiment was conducted to evaluate the usability of the BP Coach, ChatGPT-4o Mini, ChatGPT-4o and clinicians. The comparative experiment demonstrated that the BP Coach achieved significantly higher scores in utility (mean scores 4.05, SD 0.87) and completeness (mean scores 4.12, SD 0.78) when compared to ChatGPT-4o Mini, ChatGPT-4o, and clinicians. In terms of clarity, the BP Coach was slightly lower than clinicians (mean scores 4.03, SD 0.88). In addition, the BP Coach exhibited lower performance in conciseness (mean scores 3.00, SD 0.96). Clinicians reported a marked improvement in work efficiency (2.67 vs. 4.17, P &lt; .001) and experienced faster and more effective patient interactions (3.0 vs. 4.17, P = .004). Furthermore, the Hyper-DREAM platform significantly decreased work intensity (2.5 vs. 3.5, P = .01) and minimized disruptions to daily routines (2.33 vs. 3.55, P = .004). The Hyper-DREAM platform demonstrated significantly greater overall satisfaction compared to the WeChat-based standard management (3.33 vs. 4.17, P = .01). Additionally, clinicians exhibited a markedly higher willingness to integrate the Hyper-DREAM platform into clinical practice (2.67 vs. 4.17, P &lt; .001). Furthermore, patient management time decreased from 11.5 min (SD 1.87) with Wechat-based standard management to 7.5 min (SD 1.84, P = .01) with Hyper-DREAM. Hypertensive patients reported high satisfaction with the Hyper-DREAM platform, including ease of use (mean scores 1.60, SD 0.69), system information arrangement (mean scores 1.69, SD 0.71), and usefulness (mean scores 1.57, SD 0.58). In conclusion, our study presents Hyper-DREAM, a novel artificial intelligence-driven platform for hypertension management, designed to alleviate clinicia","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"42"},"PeriodicalIF":3.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Selection in Breast Cancer Gene Expression Data Using KAO and AOA with SVM Classification.","authors":"Abrar Yaqoob, Navneet Kumar Verma","doi":"10.1007/s10916-025-02171-6","DOIUrl":"https://doi.org/10.1007/s10916-025-02171-6","url":null,"abstract":"<p><p>Breast cancer classification using gene expression data presents significant challenges due to high dimensionality and complexity. This study introduces a novel hybrid framework integrating the Kashmiri Apple Optimization Algorithm (KAO) and the Armadillo Optimization Algorithm (AOA) for effective feature selection, coupled with Support Vector Machines (SVM) for precise classification. The dual-stage approach leverages KAO for global exploration of informative genes and AOA for refining the selection through local optimization, addressing issues of redundancy and premature convergence. Applied to breast cancer datasets, the proposed method achieved a classification accuracy of 98.97%, precision of 98.46%, recall of 100%, and an F1-score of 99.22% using a subset of 15 genes. The robustness of the framework was validated across varying subset sizes, demonstrating consistent high performance. By optimizing feature relevance and redundancy, the KAO-AOA framework provides a promising tool for gene-based cancer prediction with potential applications to other cancer datasets and real-world clinical use.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"40"},"PeriodicalIF":3.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143719889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models' Responses to Spinal Cord Injury: A Comparative Study of Performance.","authors":"Jinze Li, Chao Chang, Yanqiu Li, Shengyu Cui, Fan Yuan, Zhuojun Li, Xinyu Wang, Kang Li, Yuxin Feng, Zuowei Wang, Zhijian Wei, Fengzeng Jian","doi":"10.1007/s10916-025-02170-7","DOIUrl":"https://doi.org/10.1007/s10916-025-02170-7","url":null,"abstract":"<p><p>With the increasing application of large language models (LLMs) in the medical field, their potential in patient education and clinical decision support is becoming increasingly prominent. Given the complex pathogenesis, diverse treatment options, and lengthy rehabilitation periods of spinal cord injury (SCI), patients are increasingly turning to advanced online resources to obtain relevant medical information. This study analyzed responses from four LLMs-ChatGPT-4o, Claude-3.5 sonnet, Gemini-1.5 Pro, and Llama-3.1-to 37 SCI-related questions spanning pathogenesis, risk factors, clinical features, diagnostics, treatments, and prognosis. Quality and readability were assessed using the Ensuring Quality Information for Patients (EQIP) tool and Flesch-Kincaid metrics, respectively. Accuracy was independently scored by three senior spine surgeons using consensus scoring. Performance varied among the models. Gemini ranked highest in EQIP scores, suggesting superior information quality. Although the readability of all four LLMs was generally low, requiring a college-level reading comprehension ability, they were all able to effectively simplify complex content. Notably, ChatGPT led in accuracy, achieving significantly higher \"Good\" ratings (83.8%) compared to Claude (78.4%), Gemini (54.1%), and Llama (62.2%). Comprehensiveness scores were high across all models. Furthermore, the LLMs exhibited strong self-correction abilities. After being prompted for revision, the accuracy of ChatGPT and Claude's responses improved by 100% and 50%, respectively; both Gemini and Llama improved by 67%. This study represents the first systematic comparison of leading LLMs in the context of SCI. While Gemini excelled in response quality, ChatGPT provided the most accurate and comprehensive responses.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"39"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals.","authors":"Mehmet Ali Gelen, Turker Tuncer, Mehmet Baygin, Sengul Dogan, Prabal Datta Barua, Ru-San Tan, U R Acharya","doi":"10.1007/s10916-025-02169-0","DOIUrl":"10.1007/s10916-025-02169-0","url":null,"abstract":"<p><strong>Background and purpose: </strong>Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals.</p><p><strong>Method: </strong>We have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion.</p><p><strong>Results: </strong>Our proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation.</p><p><strong>Conclusion: </strong>Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"38"},"PeriodicalIF":3.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143700651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative Technologies for Smarter and Efficient Operating Room Scheduling.","authors":"Valentina Bellini, Tania Domenichetti, Elena Giovanna Bignami","doi":"10.1007/s10916-025-02168-1","DOIUrl":"https://doi.org/10.1007/s10916-025-02168-1","url":null,"abstract":"<p><p>An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., which developed a predictive model of surgical case durations. The model appears to adopt a pragmatic approach by analyzing tangible variables and undergoing validation across various types of surgical procedures, which suggests potential avenues for enhancing efficiency and sustainability in healthcare practices. However, we have some observations, particularly regarding the feasibility and practical implementation of the proposed model. A key limitation of the model is the precise definition of surgical duration, which requires further specification. To effectively translate the model into a practical scheduling approach, it is essential to consider total Operating Room (OR) occupancy time as a critical determinant of surgical planning and resource allocation. This includes not only the actual procedural time but also preoperative preparation, anesthesia induction and recovery, cleaning, and material restocking, all of which significantly impact overall scheduling efficiency. Another critical aspect concerns the quality and reliability of the input data, which is fundamental for ensuring the accuracy and effectiveness of the model. Furthermore, the adoption of new technologies should be regarded not merely as an innovation but as a means to develop high-performance, efficient tools that enhance current clinical practice. In this context, machine learning models should not only serve as analytical instruments but also as actionable tools, enabling the transition from predictive insights to strategic planning and optimized scheduling, ultimately improving decision-making and resource allocation. While making accurate predictions is a good starting point, maintaining an active AI model requires investment in resources, such as an increase in the number of surgical cases compared to the current organizational system. It may be beneficial to consider the creation of a multidisciplinary group that could promote the integration of AI with other emerging technologies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"49 1","pages":"37"},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}