首页 > 最新文献

Journal of Medical Systems最新文献

英文 中文
Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System 识别在电子病历系统中实施人工智能辅助临床决策支持的促进因素和障碍
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-18 DOI: 10.1007/s10916-024-02104-9
Joseph Finkelstein, Aileen Gabriel, Susanna Schmer, Tuyet-Trinh Truong, Andrew Dunn

Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.

近年来,计算技术的进步推动了人工智能(AI)医疗保健技术的发展。集成到电子健康记录(EHR)中的人工智能辅助临床决策支持(CDS)被证明在改善临床护理方面具有巨大潜力。随着人工智能辅助临床决策支持的迅速普及,人们意识到,如果不仔细考虑与这些工具的实施和维护有关的社会技术问题,就会导致意想不到的后果,错失良机,并使这些潜在的有用技术得不到最佳利用。48 小时出院预测工具(48DPT)是一种新的人工智能辅助电子病历 CDS,用于促进出院规划。本研究旨在从方法学角度评估 48DPT 的实施情况,并使用经过验证的实施科学框架确定采用和维护的障碍和促进因素。研究采用了 RE-AIM(Reach、Effectiveness、Adoption、Implementation、Maintenance)的主要维度和实施研究综合框架(CFIR)的构架,对使用 48DPT 的 24 位主要利益相关者进行了访谈分析。通过对 48DPT 实施情况的系统评估,我们描述了实施过程中的促进因素和障碍,如缺乏认识、缺乏准确性和信任、可及性有限以及透明度等。根据我们的评估,确定了人工智能辅助电子病历 CDS 成功实施的关键因素。未来人工智能辅助电子病历数据采集系统的实施工作应从项目一开始就让主要的临床利益相关者参与人工智能工具的开发,支持人工智能模型的透明度和可解释性,为临床用户提供持续的教育和入职培训,并从临床人员那里获得有关数据采集系统性能的持续意见。
{"title":"Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System","authors":"Joseph Finkelstein, Aileen Gabriel, Susanna Schmer, Tuyet-Trinh Truong, Andrew Dunn","doi":"10.1007/s10916-024-02104-9","DOIUrl":"https://doi.org/10.1007/s10916-024-02104-9","url":null,"abstract":"<p>Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"14 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268618","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}
引用次数: 0
Exploring Clinical Practices of Critical Alarm Settings in Intensive Care Units: A Retrospective Study of 60,000 Patient Stays from the MIMIC-IV Database 重症监护病房危重症警报设置的临床实践探索:对 MIMIC-IV 数据库中 60,000 例住院患者的回顾性研究
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-16 DOI: 10.1007/s10916-024-02107-6
Remi Carencotte, Matthieu Oliver, Nicolas Allou, Cyril Ferdynus, Jérôme Allyn

In Intensive Care Unit (ICU), the settings of the critical alarms should be sensitive and patient-specific to detect signs of deteriorating health without ringing continuously, but alarm thresholds are not always calibrated to operate this way. An assessment of the connection between critical alarm threshold settings and the patient-specific variables in ICU would deepen our understanding of the issue. The aim of this retrospective descriptive and exploratory study was to assess this relationship using a large cohort of ICU patient stays. A retrospective study was conducted on some 70,000 ICU stays taken from the MIMIC-IV database. Critical alarm threshold values and threshold modification frequencies were examined. The link between these alarm threshold settings and 30 patient variables was then explored by computing the Shapley values of a Random Tree Forest model, fitted with patient variables and alarm settings. The study included 57,667 ICU patient stays. Alarm threshold values and alarm threshold modification frequencies exhibited the same trend: they were influenced by the vital sign monitored, but almost never by the patient’s overall health status. This exploratory study also placed patients’ vital signs as the most important variables, far ahead of medication. In conclusion, alarm settings were rigid and mechanical and were rarely adapted to the evolution of the patient. The management of alarms in ICU appears to be imperfect, and a different approach could result in better patient care and improved quality of life at work for staff.

在重症监护病房(ICU)中,危急报警器的设置应灵敏并针对患者的具体情况,以便在不持续响铃的情况下检测到健康状况恶化的迹象,但报警器的阈值并不总是按照这种方式进行校准。对重症监护病房危重症警报阈值设置与患者特定变量之间的联系进行评估将加深我们对这一问题的理解。这项回顾性描述和探索性研究的目的是利用一大批重症监护病房患者的住院情况来评估这种关系。我们从 MIMIC-IV 数据库中抽取了约 70,000 例重症监护病房住院病例进行了回顾性研究。对临界警报阈值和阈值修改频率进行了研究。然后,通过计算随机树森林模型的夏普利值(Shapley values)来探索这些警报阈值设置与 30 个患者变量之间的联系,该模型与患者变量和警报设置相匹配。该研究包括 57,667 次重症监护病房患者住院。警报阈值和警报阈值修改频率呈现出相同的趋势:它们受监测的生命体征影响,但几乎不受患者整体健康状态的影响。这项探索性研究还将患者的生命体征列为最重要的变量,远远高于药物治疗。总之,警报设置是僵化和机械的,很少能适应病人的变化。重症监护室的警报管理似乎并不完善,如果采用不同的方法,就能更好地护理病人,提高员工的工作和生活质量。
{"title":"Exploring Clinical Practices of Critical Alarm Settings in Intensive Care Units: A Retrospective Study of 60,000 Patient Stays from the MIMIC-IV Database","authors":"Remi Carencotte, Matthieu Oliver, Nicolas Allou, Cyril Ferdynus, Jérôme Allyn","doi":"10.1007/s10916-024-02107-6","DOIUrl":"https://doi.org/10.1007/s10916-024-02107-6","url":null,"abstract":"<p>In Intensive Care Unit (ICU), the settings of the critical alarms should be sensitive and patient-specific to detect signs of deteriorating health without ringing continuously, but alarm thresholds are not always calibrated to operate this way. An assessment of the connection between critical alarm threshold settings and the patient-specific variables in ICU would deepen our understanding of the issue. The aim of this retrospective descriptive and exploratory study was to assess this relationship using a large cohort of ICU patient stays. A retrospective study was conducted on some 70,000 ICU stays taken from the MIMIC-IV database. Critical alarm threshold values and threshold modification frequencies were examined. The link between these alarm threshold settings and 30 patient variables was then explored by computing the Shapley values of a Random Tree Forest model, fitted with patient variables and alarm settings. The study included 57,667 ICU patient stays. Alarm threshold values and alarm threshold modification frequencies exhibited the same trend: they were influenced by the vital sign monitored, but almost never by the patient’s overall health status. This exploratory study also placed patients’ vital signs as the most important variables, far ahead of medication. In conclusion, alarm settings were rigid and mechanical and were rarely adapted to the evolution of the patient. The management of alarms in ICU appears to be imperfect, and a different approach could result in better patient care and improved quality of life at work for staff.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"46 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268724","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}
引用次数: 0
The Potential for a Propofol Volume and Dosing Decision Support Tool in an Electronic Health Record System to Provide Anticipated Propofol Volumes and Reduce Waste 电子病历系统中的丙泊酚用量和剂量决策支持工具在提供预期丙泊酚用量和减少浪费方面的潜力
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-14 DOI: 10.1007/s10916-024-02108-5
Greg R. Johnson, Ian Yuan, Olivia Nelson, Umberto Gidaro, Larry Sloberman, Brad Feng, Ari Y. Weintraub, Kha Tran, Allan F. Simpao
{"title":"The Potential for a Propofol Volume and Dosing Decision Support Tool in an Electronic Health Record System to Provide Anticipated Propofol Volumes and Reduce Waste","authors":"Greg R. Johnson, Ian Yuan, Olivia Nelson, Umberto Gidaro, Larry Sloberman, Brad Feng, Ari Y. Weintraub, Kha Tran, Allan F. Simpao","doi":"10.1007/s10916-024-02108-5","DOIUrl":"https://doi.org/10.1007/s10916-024-02108-5","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"65 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252457","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}
引用次数: 0
Correction to: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions. 更正:痴呆症预测的机器学习:系统回顾与未来研究方向》。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-14 DOI: 10.1007/s10916-024-02109-4
Ashir Javeed, Ana Luiza Dallora, Johan Sanmartin Berglund, Arif Ali, Liaqat Ali, Peter Anderberg
{"title":"Correction to: Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.","authors":"Ashir Javeed, Ana Luiza Dallora, Johan Sanmartin Berglund, Arif Ali, Liaqat Ali, Peter Anderberg","doi":"10.1007/s10916-024-02109-4","DOIUrl":"https://doi.org/10.1007/s10916-024-02109-4","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"87"},"PeriodicalIF":3.5,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289372","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}
引用次数: 0
Assessing the Relationship between Hospital Process Digitalization and Hospital Quality – Evidence from Germany 评估医院流程数字化与医院质量之间的关系--来自德国的证据
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-13 DOI: 10.1007/s10916-024-02101-y
Justus Vogel, Alexander Haering, David Kuklinski, Alexander Geissler

Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions’ scores logically associated with an indicator, and (3) sub-dimensions’ separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p < 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p < 0.01 for models (1) and (2), p < 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators’ inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.

医院数字化旨在提高效率、降低成本和/或改善医疗质量。为了评估数字化与质量之间的关系,我们研究了流程数字化与流程和结果质量之间的关联。我们使用了 2021 年德国数字雷达(DR)项目的数据,并将这些数据与两个流程指标(股骨骨折后骨合成和髋关节置换手术的术前等待时间,n = 516 和 574)和两个结果质量指标(门诊获得性肺炎住院患者的死亡率,n = 1,074 ;新褥疮病例的比率,n = 1,519 )相结合。对于每个指标,我们都进行了单变量和多变量回归。我们通过指定三个具有不同解释变量的模型来全面衡量流程数字化程度:(1) DR 总分(0(未数字化)至 100(完全数字化)),(2) DR 分值子维度与指标逻辑相关的分数总和,(3) 子维度的单独分数。就流程质量指标而言,除一个指标外,其他指标之间的关联都不显著。DR 评分越高,肺炎患者的死亡率越低(多元回归中的 p < 0.10)。相反,流程数字化程度越高,褥疮病例比例越高(模型(1)和(2)中的 p < 0.01,模型(3)中两个子维度的 p < 0.05)。关于褥疮,我们的发现可能是由于数字化使褥疮病例的诊断、记录和报告更完善,而不是质量更差。不显著和不确定的结果可能是由于指标无法反映医院之间的质量差异和数字化效应。在未来的研究中,我们建议利用纵向数据调查医院内部的影响。
{"title":"Assessing the Relationship between Hospital Process Digitalization and Hospital Quality – Evidence from Germany","authors":"Justus Vogel, Alexander Haering, David Kuklinski, Alexander Geissler","doi":"10.1007/s10916-024-02101-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02101-y","url":null,"abstract":"<p>Hospital digitalization aims to increase efficiency, reduce costs, and/ or improve quality of care. To assess a digitalization-quality relationship, we investigate the association between process digitalization and process and outcome quality. We use data from the German DigitalRadar (DR) project from 2021 and combine these data with two process (preoperative waiting time for osteosynthesis and hip replacement surgery after femur fracture, n = 516 and 574) and two outcome quality indicators (mortality ratio of patients hospitalized for outpatient-acquired pneumonia, n = 1,074; ratio of new decubitus cases, n = 1,519). For each indicator, we run a univariate and a multivariate regression. We measure process digitalization holistically by specifying three models with different explanatory variables: (1) the total DR-score (0 (not digitalized) to 100 (fully digitalized)), (2) the sum of DR-score sub-dimensions’ scores logically associated with an indicator, and (3) sub-dimensions’ separate scores. For the process quality indicators, all but one of the associations are insignificant. A greater DR-score is weakly associated with a lower mortality ratio of pneumonia patients (p &lt; 0.10 in the multivariate regression). In contrast, higher process digitalization is significantly associated with a higher ratio of decubitus cases (p &lt; 0.01 for models (1) and (2), p &lt; 0.05 for two sub-dimensions in model (3)). Regarding decubitus, our finding might be due to better diagnosis, documentation, and reporting of decubitus cases due to digitalization rather than worse quality. Insignificant and inconclusive results might be due to the indicators’ inability to reflect quality variation and digitalization effects between hospitals. For future research, we recommend investigating within hospital effects with longitudinal data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"65 3 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207581","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}
引用次数: 0
Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review 医学图像分析中视觉变换器与卷积神经网络的比较:系统综述
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-12 DOI: 10.1007/s10916-024-02105-8
Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.

在利用人工智能(AI)快速发展的医学图像分析领域,选择合适的计算模型对于准确诊断和患者护理至关重要。本文献综述全面比较了视觉变换器(ViT)和卷积神经网络(CNN)这两种医学影像深度学习领域的领先技术。我们进行了系统的调查。我们特别关注了这些模型在处理复杂医学数据集时的鲁棒性、计算效率、可扩展性和准确性。综述纳入了 36 项研究的结果,并指出了一个共同的趋势,即基于变压器的模型,尤其是 ViT,在各种医学成像任务中展现出巨大的潜力,与传统 CNN 模型相比表现出更优越的性能。此外,预训练对于变压器的应用显然非常重要。我们希望这项工作能帮助研究人员和从业人员根据该领域的技术现状和未来趋势,为特定的医学图像分析任务选择最合适的模型。
{"title":"Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review","authors":"Satoshi Takahashi, Yusuke Sakaguchi, Nobuji Kouno, Ken Takasawa, Kenichi Ishizu, Yu Akagi, Rina Aoyama, Naoki Teraya, Amina Bolatkan, Norio Shinkai, Hidenori Machino, Kazuma Kobayashi, Ken Asada, Masaaki Komatsu, Syuzo Kaneko, Masashi Sugiyama, Ryuji Hamamoto","doi":"10.1007/s10916-024-02105-8","DOIUrl":"https://doi.org/10.1007/s10916-024-02105-8","url":null,"abstract":"<p>In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"14 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207582","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}
引用次数: 0
Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination GPT-4 V 对日本全国临床工程师执业资格考试的反应分析
IF 5.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-11 DOI: 10.1007/s10916-024-02103-w
Kai Ishida, Naoya Arisaka, Kiyotaka Fujii

Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description.

聊天生成预训练转换器(ChatGPT;OpenAI)是一种先进的大型语言模型,可根据用户输入模拟类似人类的对话。在日本全国临床工程师执业资格考试中,我们使用 2012 年至 2023 年的 2,155 道题目对 GPT-4 V 的性能进行了评估。所有问题的平均正确率为 86.0%。其中,临床医学、基础医学、医用材料、生物特性和机械工程的正确答题率≥90%。相反,医疗设备安全管理、电子电气工程和体外循环的正确答题率较低,从 64.8% 到 76.5%不等。包含数字/表格、需要数字计算、数字/表格∩计算和日本工业标准知识的题目的正确答对率分别为 55.2%、85.8%、64.2% 和 31.0%。答对率低的原因是 ChatGPT 缺乏对图像的识别以及标准和法律知识。本研究的结论是,在使用 ChatGPT 时需要小心谨慎,因为它的一些解释缺乏正确的描述。
{"title":"Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination","authors":"Kai Ishida, Naoya Arisaka, Kiyotaka Fujii","doi":"10.1007/s10916-024-02103-w","DOIUrl":"https://doi.org/10.1007/s10916-024-02103-w","url":null,"abstract":"<p>Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4 V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"12 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207595","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}
引用次数: 0
Optimizing Medical Care during a Nerve Agent Mass Casualty Incident Using Computer Simulation. 利用计算机模拟优化神经毒剂大规模伤亡事件中的医疗护理。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-05 DOI: 10.1007/s10916-024-02094-8
De Rouck Ruben, Mehdi Benhassine, Debacker Michel, Van Utterbeeck Filip, Dhondt Erwin, Hubloue Ives

Introduction: Chemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.

Methods: We created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy ('Scoop and Run' vs. 'Stay and Play'), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.

Results: Only evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.

Conclusion: Our findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.

导言:化学大规模伤亡事件(MCIs)对公众健康和安全构成严重威胁,有可能使医疗基础设施不堪重负,造成社会混乱。计算机模拟系统因其通用性、成本效益和较低的道德问题易感性,正在成为验证这些计划的既定机制:我们创建了一个类似于 1995 年东京沙林事件的城市地铁沙林袭击计算机模拟模型。我们利用 SIMEDIS 计算机模拟器创建并组合了疏散、扩散和受害者模型。我们分析了多种可能方法的影响,如疏散政策("舀起就跑 "与 "留下来玩")、三种策略(现场洗消与稳定、场外洗消与稳定、现场稳定与场外洗消)、初步分流、受害者分布方法、运输监督技能水平以及搜救能力的影响:结果:只有疏散政策、策略和初步分流对死亡率有显著影响。总平均死亡率从场外洗消和 "舀起就跑 "政策与初步分流相结合的 14.7 例死亡,到现场洗消和 "留下来玩 "政策与不进行初步分流相结合的 24 例死亡不等:我们的研究结果表明,在模拟的城市化学创伤事件中,采用现场洗消的 "留守与游玩 "方法会比采用医院洗消的 "舀起就跑 "方法导致更糟糕的结果。快速运送受害者并在现场施用解毒剂有可能挽救最多的生命,因为这样可以更快地到达医院进行最终治疗。
{"title":"Optimizing Medical Care during a Nerve Agent Mass Casualty Incident Using Computer Simulation.","authors":"De Rouck Ruben, Mehdi Benhassine, Debacker Michel, Van Utterbeeck Filip, Dhondt Erwin, Hubloue Ives","doi":"10.1007/s10916-024-02094-8","DOIUrl":"10.1007/s10916-024-02094-8","url":null,"abstract":"<p><strong>Introduction: </strong>Chemical mass casualty incidents (MCIs) pose a substantial threat to public health and safety, with the capacity to overwhelm healthcare infrastructure and create societal disorder. Computer simulation systems are becoming an established mechanism to validate these plans due to their versatility, cost-effectiveness and lower susceptibility to ethical problems.</p><p><strong>Methods: </strong>We created a computer simulation model of an urban subway sarin attack analogous to the 1995 Tokyo sarin incident. We created and combined evacuation, dispersion and victim models with the SIMEDIS computer simulator. We analyzed the effect of several possible approaches such as evacuation policy ('Scoop and Run' vs. 'Stay and Play'), three strategies (on-site decontamination and stabilization, off-site decontamination and stabilization, and on-site stabilization with off-site decontamination), preliminary triage, victim distribution methods, transport supervision skill level, and the effect of search and rescue capacity.</p><p><strong>Results: </strong>Only evacuation policy, strategy and preliminary triage show significant effects on mortality. The total average mortality ranges from 14.7 deaths in the combination of off-site decontamination and Scoop and Run policy with pretriage, to 24 in the combination of onsite decontamination with the Stay and Play and no pretriage.</p><p><strong>Conclusion: </strong>Our findings suggest that in a simulated urban chemical MCI, a Stay and Play approach with on-site decontamination will lead to worse outcomes than a Scoop and Run approach with hospital-based decontamination. Quick transport of victims in combination with on-site antidote administration has the potential to save the most lives, due to faster hospital arrival for definitive care.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"82"},"PeriodicalIF":3.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11377464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132918","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}
引用次数: 0
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction. 医学 LLM 的新型评估框架:结合模糊逻辑和 MCDM 以提取医学关系和临床概念
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-31 DOI: 10.1007/s10916-024-02090-y
A H Alamoodi, Omar Zughoul, Dianese David, Salem Garfan, Dragan Pamucar, O S Albahri, A S Albahri, Salman Yussof, Iman Mohamad Sharaf

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( A 4 ) "GatorTron S 10B" had the 1st rank as compared to ( A 1 ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

人工智能(AI)已成为现代科技的重要组成部分,尤其是在医疗保健领域,这一点从大型语言模型(LLM)的不断发展中就能明显看出,这些模型被广泛应用于包括医疗在内的各个领域。然而,在医疗领域使用这些 LLMs 时,需要一个评估平台来确定其适用性并推动未来的开发工作。为此,本研究旨在通过开发一种专门用于评估医学 LLM 的综合多标准决策(MCDM)方法来解决这一问题。人工智能(尤其是 LLM)在医疗保健领域的成功取决于其有效性、安全性和伦理合规性。因此,必须有一个强大的评估框架,以便将其融入医疗环境。本研究建议使用模糊加权零不一致(FWZIC)方法扩展到 p, q-quasirung orthopair 模糊集(p, q-QROFS)来权衡评价标准。这一扩展可处理医疗决策过程中固有的不确定性。这种方法通过结合模糊逻辑原理,适应了现实世界中医疗数据和标准的不精确性和多面性。在本研究的案例研究中,采用了多分配理想-真实比较分析(MAIRCA)方法来评估医学 LLM。研究结果显示,"医学关系提取 "标准及其子级别的重要性(0.504)高于 "临床概念提取 "标准的重要性(0.495)。就所评估的 LLM 而言,在 6 个备选方案中,(A 4)"GatorTron S 10B "排名第一,而(A 1)"GatorTron 90B "排名第六。本研究的意义超出了学术讨论的范围,直接影响到医疗实践和患者的治疗效果。所提出的框架可以帮助医护人员在医疗环境中采用和使用 LLM 时做出更明智的决定。
{"title":"A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.","authors":"A H Alamoodi, Omar Zughoul, Dianese David, Salem Garfan, Dragan Pamucar, O S Albahri, A S Albahri, Salman Yussof, Iman Mohamad Sharaf","doi":"10.1007/s10916-024-02090-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02090-y","url":null,"abstract":"<p><p>Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that \"Medical Relation Extraction\" criteria with its sub-levels had more importance with (0.504) than \"Clinical Concept Extraction\" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( <math><mrow><mi>A</mi> <mn>4</mn></mrow> </math> ) \"GatorTron S 10B\" had the 1st rank as compared to ( <math><mrow><mi>A</mi> <mn>1</mn></mrow> </math> ) \"GatorTron 90B\" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"81"},"PeriodicalIF":3.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142108163","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}
引用次数: 0
Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities. 用于伤口评估和监测的移动应用程序:局限、进步与机遇。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-24 DOI: 10.1007/s10916-024-02091-x
Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

随着伤口评估应用程序在各种应用程序商店中大量涌现,以及人工智能(AI)在医疗保健应用程序中的不断融入,人们对综合评估系统的需求日益增长。目前的应用程序缺乏足够的循证可靠性,因此有必要进行系统评估。本研究的目的是评估伤口评估和监测应用程序,找出其局限性,并概述未来应用程序开发的机遇。我们在两大应用商店(Google Play 商店和苹果应用商店)进行了电子搜索,并由三名独立评分员对所选应用进行评分。共发现了 170 个应用程序,根据一系列纳入和排除标准,选出了 10 个进行审查。通过修改现有量表,我们创建了伤口评估应用程序评级量表,并用于评估所选的 10 款应用程序。我们的评分表主要评估应用程序的功能和软件质量特征。根据我们的评估,应用程序商店中的大多数应用程序都不符合伤口监测和评估的总体要求。我们评测的所有应用程序都主要面向从业人员和医生。根据我们的评估,ImitoWound 应用程序的平均得分最高,为 4.24 分。但在 11 项功能标准中,该应用程序只有 7 项标准。最后,我们建议未来有机会利用先进技术,特别是涉及人工智能的技术,来增强伤口评估应用程序的功能和功效。这项研究为未来的开发人员和研究人员提供了宝贵的资源,帮助他们改进基于伤口评估的应用程序的设计,包括软件质量和功能的改进。
{"title":"Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.","authors":"Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed","doi":"10.1007/s10916-024-02091-x","DOIUrl":"10.1007/s10916-024-02091-x","url":null,"abstract":"<p><p>With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"48 1","pages":"80"},"PeriodicalIF":3.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046805","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}
引用次数: 0
期刊
Journal of Medical Systems
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1