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Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol. 在儿科人群中通过心电图进行无创血糖事件检测的人工智能:研究方案。
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2023-01-01 Epub Date: 2023-01-23 DOI: 10.1007/s12553-022-00719-x
Martina Andellini, Salman Haleem, Massimiliano Angelini, Matteo Ritrovato, Riccardo Schiaffini, Ernesto Iadanza, Leandro Pecchia

Purpose: Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device.

Methods: This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.

Results: Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm.

Conclusion: This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions.

Trial registration: ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.

Supplementary information: The online version contains supplementary material available at 10.1007/s12553-022-00719-x.

目的:由于血糖控制不佳,儿童 1 型糖尿病 (T1D) 患者发生严重低血糖和高血糖事件的风险更大。为了降低不良事件的风险,患者需要通过频繁使用手指点刺或连续血糖监测系统(CGM)进行血糖监测,以尽可能实现最佳的血糖管理。然而,目前已经提出了几种非侵入性技术,旨在利用基于血糖水平的生理参数变化。本研究的总体目标是验证一种基于人工智能(AI)的算法,利用通过无创设备收集的心电图信号检测血糖事件:本研究将招募已经使用 CGM 的 T1D 儿科参与者。参与者将佩戴额外的无创可穿戴设备,用于记录生理数据和呼吸频率。主要结果是通过心电图变量进行血糖测量。收集到的数据将用于设计、开发和验证基于深度学习(DL)人工智能算法的个性化和通用分类器,该算法能够通过使用可穿戴设备记录的少量心电图自动检测低血糖事件:数据收集工作预计将于 2023 年 6 月左右完成。预计将收集到足够的数据来开发和验证人工智能算法:这是一项验证研究,将在更大的糖尿病样本人群中进行更多测试,以验证之前基于四名健康成人的试点结果,为人工智能算法在自由生活条件下检测儿科糖尿病患者血糖事件的可靠性提供证据:试验注册:ClinicalTrials.gov identifier:NCT03936634。注册日期:2022 年 3 月 11 日,回顾性注册,https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.补充信息:在线版本包含补充材料,可查阅 10.1007/s12553-022-00719-x。
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引用次数: 0
Survival study on deep learning techniques for IoT enabled smart healthcare system. 物联网智能医疗系统深度学习技术的生存研究。
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2023-01-01 DOI: 10.1007/s12553-023-00736-4
Ashok Kumar Munnangi, Satheeshwaran UdhayaKumar, Vinayakumar Ravi, Ramesh Sekaran, Suthendran Kannan

Purpose: The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare.

Methods: A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced.

Results: Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%.

Conclusion: MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

目的:本文旨在回顾深度学习(DL)技术在医疗保健领域的应用情况,并重点介绍现有方法的优势和缺点,以及一些研究最后通牒。我们的研究为医疗保健专业人员和政府提供了基于dl的智能医疗保健数据分析的基础。方法:设计了一种基于深度学习的技术来提取传感器位移效应,并通过人工智能(AI)预测活动识别的异常情况。该技术最大限度地减少了递归神经网络(RNN)的梯度消失问题,从而减少了考虑时间和空间因素的异常检测时间。介绍了一种基于Moran自相关和回归的Elman递归神经网络(MAR-ERNN)。结果:实验结果表明了该方法的可行性。结果表明,该方法的准确率提高了95%,执行时间缩短了18%。结论:MAR-ERNN对健康状态的活动识别有较好的效果。总的来说,这种支持物联网的智能医疗保健系统可以提高准确性,并最大限度地减少时间和开销。
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引用次数: 3
Science diplomacy in medical physics - an international perspective. 医学物理学中的科学外交——国际视野。
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2023-01-01 Epub Date: 2023-05-06 DOI: 10.1007/s12553-023-00756-0
Eva Bezak, Cari Borrás, Francis Hasford, Nupur Karmaker, Angela Keyser, Magdalena Stoeva, Christoph Trauernicht, Hong Chai Yeong, Loredana G Marcu

Purpose: Science diplomacy in medical physics is a relatively young research field and translational practice that focuses on establishing international collaborations to address some of the questions biomedical professionals face globally. This paper aims to present an overview of science diplomacy in medical physics, from an international perspective, illustrating the ways collaborations within and across continents can lead to scientific and professional achievements that advance scientific growth and improve patients care.

Methods: Science diplomacy actions were sought that promote collaborations in medical physics across the continents, related to professional and scientific aspects alike.

Results: Several science diplomacy actions have been identified to promote education and training, to facilitate research and development, to effectively communicate science to the public, to enable equitable access of patients to healthcare and to focus on gender equity within the profession as well as healthcare provision. Scientific and professional organizations in the field of medical physics across all continents have adopted a number of efforts in their aims, many of them with great success, to promote science diplomacy and to foster international collaborations.

Conclusions: Professionals in medical physics can advance through international cooperation, by building strong communication across scientific communities, addressing rising demands, exchange scientific information and knowledge.

目的:医学物理学中的科学外交是一个相对年轻的研究领域和转化实践,专注于建立国际合作,以解决生物医学专业人员在全球面临的一些问题。本文旨在从国际角度概述医学物理学中的科学外交,说明各大洲内部和跨大洲的合作如何取得科学和专业成就,促进科学发展,改善患者护理。方法:寻求科学外交行动,促进各大洲在专业和科学方面的医学物理学合作。结果:已经确定了一些科学外交行动,以促进教育和培训,促进研究和开发,向公众有效宣传科学,使患者能够公平获得医疗保健,并关注职业内的性别公平以及医疗保健服务。各大洲医学物理领域的科学和专业组织为促进科学外交和促进国际合作做出了许多努力,其中许多都取得了巨大成功。结论:医学物理学专业人员可以通过国际合作、在科学界建立强有力的沟通、满足不断增长的需求、交流科学信息和知识来取得进步。
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引用次数: 1
Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan. 利用日本匿名生活普查数据进行可解释的机器学习分析,以识别糖尿病风险因素。
IF 3.1 Q2 MEDICAL INFORMATICS Pub Date : 2023-01-01 Epub Date: 2023-01-26 DOI: 10.1007/s12553-023-00730-w
Pei Jiang, Hiroyuki Suzuki, Takashi Obi

Purpose: Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods.

Methods: Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors.

Results: Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age.

Conclusions: New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

目的:糖尿病会给我们的生活带来各种问题。随着大数据社会的蓬勃发展,一些糖尿病的风险因素必然依然存在。为了在大数据社会中发现新的糖尿病风险因素,并探索进一步有效利用大数据,我们使用可解释的机器学习方法分析了有关日本市民生活状况调查的非客观普查数据:方法:使用了七种可解释的机器学习方法来分析日本公民普查数据。首先,使用逻辑分析法从 19 个选定的初始要素中分析糖尿病的风险因素。然后,使用线性分析、线性判别分析、林量化分析方法 2、随机森林、XGBoost 和 SHAP 方法重新检查并找出不同因素的贡献。最后,分析了各因素之间的关系,以了解各因素之间的关系:结果:4 个新的风险因素:家庭成员数量、保险类型、公共养老金类型和健康意识水平首次被发现为糖尿病的风险因素,另外 11 个风险因素在本次分析中被再次确认。特别是在一些可解释的模型中,保险类型因素和健康意识水平因素对糖尿病的影响大于高血压、高脂血症和压力因素。我们还发现,工作年限被认为是糖尿病的一个风险因素,因为它与年龄这一风险因素的系数很高:结论:利用可解释的机器学习模型,基于日本非客观导向的匿名人口普查数据,发现了糖尿病的新风险因素。新发现的风险因素激发了预防糖尿病的新政策。此外,我们的分析证明,在当今繁荣的社会中,大数据可以帮助我们找到有用的知识。我们的研究还为识别更多风险因素和提高大数据使用效率铺平了道路。
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引用次数: 0
ML technologies for diagnosing and treatment of tuberculosis: a survey ML技术在肺结核诊断和治疗中的应用综述
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2023-01-01 DOI: 10.1007/s12553-023-00727-5
Joane Jonathan, A. Barakabitze
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引用次数: 0
Application program to detect unrecognized information regarding malignant tumors in radiology reports 用于检测放射学报告中有关恶性肿瘤的未识别信息的应用程序
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2022-12-31 DOI: 10.1007/s12553-022-00724-0
Shinichiroh Yokota, Shunsuke Doi, M. Fukuhara, Tomohiro Mitani, Satomi Nagashima, W. Gonoi, T. Imai, K. Ohe
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引用次数: 0
Breast cancer classification along with feature prioritization using machine learning algorithms 使用机器学习算法的乳腺癌分类和特征优先级
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2022-11-01 DOI: 10.1007/s12553-022-00710-6
A. Nahid, Md. Johir Raihan, Abdullah Al-Mamun Bulbul
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引用次数: 0
Hybrid deep boosting ensembles for histopathological breast cancer classification 混合深度增强集合用于组织病理学乳腺癌分类
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2022-11-01 DOI: 10.1007/s12553-022-00709-z
F. Nakach, Hasnae Zerouaoui, A. Idri
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引用次数: 3
Combining deep-wavelet neural networks and support-vector machines to classify breast lesions in thermography images 结合深度小波神经网络和支持向量机对乳腺热成像图像中的病变进行分类
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2022-11-01 DOI: 10.1007/s12553-022-00705-3
M. A. de Santana, Valter Augusto de Freitas Barbosa, Rita de Cássia Fernandes de Lima, W. P. dos Santos
{"title":"Combining deep-wavelet neural networks and support-vector machines to classify breast lesions in thermography images","authors":"M. A. de Santana, Valter Augusto de Freitas Barbosa, Rita de Cássia Fernandes de Lima, W. P. dos Santos","doi":"10.1007/s12553-022-00705-3","DOIUrl":"https://doi.org/10.1007/s12553-022-00705-3","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"24 1","pages":"1183 - 1195"},"PeriodicalIF":2.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78529359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A non-linear time series based artificial intelligence model to predict outcome in cardiac surgery 基于非线性时间序列的人工智能模型预测心脏手术预后
IF 2.5 Q2 MEDICAL INFORMATICS Pub Date : 2022-11-01 DOI: 10.1007/s12553-022-00706-2
Sushant Konar, Nitin Auluck, R. Ganesan, A. Goyal, Tarunpreet Kaur, Mansi Sahi, T. Samra, S. Thingnam, G. Puri
{"title":"A non-linear time series based artificial intelligence model to predict outcome in cardiac surgery","authors":"Sushant Konar, Nitin Auluck, R. Ganesan, A. Goyal, Tarunpreet Kaur, Mansi Sahi, T. Samra, S. Thingnam, G. Puri","doi":"10.1007/s12553-022-00706-2","DOIUrl":"https://doi.org/10.1007/s12553-022-00706-2","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"33 1","pages":"1169 - 1181"},"PeriodicalIF":2.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76742234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
期刊
Health and Technology
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