Health and Technology最新文献

{"title":"Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol.","authors":"Martina Andellini, Salman Haleem, Massimiliano Angelini, Matteo Ritrovato, Riccardo Schiaffini, Ernesto Iadanza, Leandro Pecchia","doi":"10.1007/s12553-022-00719-x","DOIUrl":"10.1007/s12553-022-00719-x","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Trial registration: </strong>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s12553-022-00719-x.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 1","pages":"145-154"},"PeriodicalIF":2.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9899724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10689425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survival study on deep learning techniques for IoT enabled smart healthcare system.","authors":"Ashok Kumar Munnangi,&nbsp;Satheeshwaran UdhayaKumar,&nbsp;Vinayakumar Ravi,&nbsp;Ramesh Sekaran,&nbsp;Suthendran Kannan","doi":"10.1007/s12553-023-00736-4","DOIUrl":"https://doi.org/10.1007/s12553-023-00736-4","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 2","pages":"215-228"},"PeriodicalIF":2.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9114963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Science diplomacy in medical physics - an international perspective.","authors":"Eva Bezak,&nbsp;Cari Borrás,&nbsp;Francis Hasford,&nbsp;Nupur Karmaker,&nbsp;Angela Keyser,&nbsp;Magdalena Stoeva,&nbsp;Christoph Trauernicht,&nbsp;Hong Chai Yeong,&nbsp;Loredana G Marcu","doi":"10.1007/s12553-023-00756-0","DOIUrl":"10.1007/s12553-023-00756-0","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>Science diplomacy actions were sought that promote collaborations in medical physics across the continents, related to professional and scientific aspects alike.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>Professionals in medical physics can advance through international cooperation, by building strong communication across scientific communities, addressing rising demands, exchange scientific information and knowledge.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 3","pages":"495-503"},"PeriodicalIF":2.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9620293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan.","authors":"Pei Jiang, Hiroyuki Suzuki, Takashi Obi","doi":"10.1007/s12553-023-00730-w","DOIUrl":"10.1007/s12553-023-00730-w","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"13 1","pages":"119-131"},"PeriodicalIF":3.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9876749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10667352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ML technologies for diagnosing and treatment of tuberculosis: a survey","authors":"Joane Jonathan, A. Barakabitze","doi":"10.1007/s12553-023-00727-5","DOIUrl":"https://doi.org/10.1007/s12553-023-00727-5","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"85 1","pages":"1-17"},"PeriodicalIF":2.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83921721","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}

{"title":"Application program to detect unrecognized information regarding malignant tumors in radiology reports","authors":"Shinichiroh Yokota, Shunsuke Doi, M. Fukuhara, Tomohiro Mitani, Satomi Nagashima, W. Gonoi, T. Imai, K. Ohe","doi":"10.1007/s12553-022-00724-0","DOIUrl":"https://doi.org/10.1007/s12553-022-00724-0","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"95 1","pages":"65-73"},"PeriodicalIF":2.5,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80414591","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}

{"title":"Breast cancer classification along with feature prioritization using machine learning algorithms","authors":"A. Nahid, Md. Johir Raihan, Abdullah Al-Mamun Bulbul","doi":"10.1007/s12553-022-00710-6","DOIUrl":"https://doi.org/10.1007/s12553-022-00710-6","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"2 1","pages":"1061 - 1069"},"PeriodicalIF":2.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76044648","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}

{"title":"Hybrid deep boosting ensembles for histopathological breast cancer classification","authors":"F. Nakach, Hasnae Zerouaoui, A. Idri","doi":"10.1007/s12553-022-00709-z","DOIUrl":"https://doi.org/10.1007/s12553-022-00709-z","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"56 1","pages":"1043 - 1060"},"PeriodicalIF":2.5,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88739150","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}

{"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}

{"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}