Health and Technology最新文献

{"title":"Of digital transformation in the healthcare (systematic review of the current state of the literature)","authors":"Mus’ab Muhammad Kakale","doi":"10.1007/s12553-023-00803-w","DOIUrl":"https://doi.org/10.1007/s12553-023-00803-w","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598432","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":"Comparative analysis of machine learning and ensemble approaches for hepatitis B prediction using data mining with synthetic minority oversampling technique","authors":"Azadeh Alizargar, Yang-Lang Chang, Tan-Hsu Tan, Tsung-Yu Liu","doi":"10.1007/s12553-023-00802-x","DOIUrl":"https://doi.org/10.1007/s12553-023-00802-x","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139214973","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":"Data management for resource optimization in medical IoT","authors":"Iqra Jan, Shabir Sofi","doi":"10.1007/s12553-023-00796-6","DOIUrl":"https://doi.org/10.1007/s12553-023-00796-6","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139217869","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":"Explainability analysis in predictive models based on machine learning techniques on the risk of hospital readmissions","authors":"Juan Camilo Lopera Bedoya, Jose Lisandro Aguilar Castro","doi":"10.1007/s12553-023-00794-8","DOIUrl":"https://doi.org/10.1007/s12553-023-00794-8","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139225333","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":"Digital twins for breast cancer treatment – an empirical study on stakeholders’ perspectives on potentials and challenges","authors":"Jens Konopik, Larissa Wolf, Oliver Schöffski","doi":"10.1007/s12553-023-00798-4","DOIUrl":"https://doi.org/10.1007/s12553-023-00798-4","url":null,"abstract":"Abstract Purpose With 2.3 million diagnoses and 685,000 deaths annually, breast cancer is the most common cancer in women. The provision of necessary information throughout the whole patient journey is key to minimize the risk of breast cancer, to detect breast cancer as early as possible, and to aid the treatment process. Digital solutions provide abilities to holistically collect, transfer, and sophisticatedly analyze information. Specifically, digital twins in healthcare, as dynamic replicas of human bodies, are promising approaches for monitoring the condition of their patients and predicting tumor developments based on biometric data. However, the acceptance and adoption of such digital twin solutions in healthcare heavily depend on the individual stakeholders of the treatment process. This study aims to identify potentials and challenges of the introduction of digital twins in breast cancer applications from the involved stakeholders’ perspectives. Methods We conducted semi-structured interviews with 14 relevant stakeholders from the breast cancer treatment process. The interviews were then analyzed, based on the qualitative content analysis according to Mayring. Results The results show that stakeholders see great potential in digital twin solutions to further facilitate personalized medicine, efficiency increases, and scientific benefits. However, the sensitive nature of healthcare causes numerous potential challenges in the technical, regulatory, user interface, and the strategic domain. Conclusions The stakeholders unanimously agreed on the potential benefits of digital twins. However, existing systemic and individual stakeholder-level barriers hamper their introduction in breast cancer settings.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992662","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":"Physical relief potential through robot-assisted mobilization in nursing care: an exploratory study","authors":"Jonathan Levin Behrens, Christian Kowalski, Anna Brinkmann, Sara Marquard, Sandra Hellmers, Maren Asmussen-Clausen, Karina Jürgensen, Stephanie Raudies, Manfred Hülsken-Giesler, Andreas Hein","doi":"10.1007/s12553-023-00795-7","DOIUrl":"https://doi.org/10.1007/s12553-023-00795-7","url":null,"abstract":"Abstract Purpose Physically demanding activities at the nursing bed are a key factor in the overwork of nursing staff and play a major role in the development of musculoskeletal disorders. The heavy back strain plays a significant part in this. Technical aids such as robotic assistance systems have the potential to minimize this overload during nursing activities. In the present work, we have investigated the relief potential of a supporting robotic assistance system developed in the AdaMeKoR project. An exploratory study design was developed to assess the relief potential of the robotic system for nurses during the care action of repositioning from the supine position to the sitting position at the edge of a nursing bed under kinaesthetic principles. Methods The study was conducted in March 2022 with a total of 21 nursing professionals participating. Safety precautions at this stage of the robot’s development made it necessary to use a 40 kg patient simulator instead of having a human act as the patient. Each participant performed the repositioning three times in the conventional manner and three times with the robotic-assistance. The conventional and the robotic-assisted task execution was compared using different perspectives of analysis. From a sensory perspective, ground reaction forces and electromyography data were collected and analyzed. A kinaesthetic perspective was added using 3D-video data which was analyzed by professional kinaesthetics trainers. A third perspective was added by collecting the subjective workload experiences of the participants. Results While participants’ self-assessment based on a NASA-TLX questionnaire suggests more of a physical and psychological strain from using the robot, electromyography shows a 24.41% reduction in muscle activity for left back extensors and 7.99% for right back extensors. The kinaesthetic visual inspection of the study participants also allows conclusions to be made that the robot assistance system has a relieving effect when performing the nursing task. Conclusions The conducted study suggests that overall the robotic-assistance has the potential of relieving nurses of partial physical exertion during mobilization. However, the different focuses of analysis show varying results in regard to external, i.e. sensor data and expert analysis, compared to internal, i.e. the nurses, perspectives. Going forward, these results have to be further expanded to get more robust analyses and insights on the interdependencies of subjective factors contributing to the experience of workload. In view of the fact that robotics for nursing is still a relatively new field and there are various lessons to be learned regarding the conceptualization of studies and corresponding evaluations, our approach of combining perspectives of analysis allows for a more differentiated view of the subject at hand.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134992834","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":"Internet-of-things based machine learning enabled medical decision support system for prediction of health issues","authors":"Manju Lata Sahu, Mithilesh Atulkar, Mitul Kumar Ahirwal, Afsar Ahamad","doi":"10.1007/s12553-023-00790-y","DOIUrl":"https://doi.org/10.1007/s12553-023-00790-y","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135037525","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":"Improvement of outpatient service processes: a case study of the university of Hong Kong-Shenzhen hospital","authors":"Jingsong Chen, Bráulio Alturas","doi":"10.1007/s12553-023-00788-6","DOIUrl":"https://doi.org/10.1007/s12553-023-00788-6","url":null,"abstract":"Abstract Purpose This work presents a case study of the University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointments registration system. The research question is to determine which factors influence patient satisfaction most. Methods The study provides an anatomy of the hospital outpatient process through various methods and theories, including a literature review, field research, expert consultation, business process improvement (BPI) theory and information technology, with the aim of identifying the objectives and strategies of the hospital for improving its outpatient process. A quantitative analysis was performed using a questionnaire survey to identify the defects and weaknesses of the current model. The principles, methods and techniques of BPI theory are used to analyse various problems existing in the outpatient process and the extent of their influence. A structural equation model has been established for scientific and quantitative analysis, which can help identify the goals of optimization and measure improvement in the outpatient process and patient satisfaction. Results It was determined the source of inefficiency of the current outpatient service process. By means of outpatient process improvement, the study aims to increase the hospital’s efficiency and raise the level of patient satisfaction so that it may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and management. Conclusions It was found that service attitude, service value and waiting time have a significant influence on patient satisfaction.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091918","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":"Artificial intelligence for healthcare in Africa: a scientometric analysis","authors":"Basile Njei, Ulrick Sidney Kanmounye, Mouhand F. Mohamed, Anim Forjindam, Nkafu Bechem Ndemazie, Adedeji Adenusi, Stella-Maris C. Egboh, Evaristus S. Chukwudike, Joao Filipe G. Monteiro, Tyler M. Berzin, Akwi W. Asombang","doi":"10.1007/s12553-023-00786-8","DOIUrl":"https://doi.org/10.1007/s12553-023-00786-8","url":null,"abstract":"","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634428","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":"Selection of consistent breath biomarkers of abnormal liver function using feature selection: a pilot study","authors":"Rakesh Kumar Patnaik, Yu-Chen Lin, Ming Chih Ho, J. Andrew Yeh","doi":"10.1007/s12553-023-00787-7","DOIUrl":"https://doi.org/10.1007/s12553-023-00787-7","url":null,"abstract":"Abstract Purpose Breath profiling has gained importance in recent years as it is a non-invasive technique to identify biomarkers for various diseases. Breath profiling of abnormal liver function in individuals for identifying potential biomarkers in exhaled breath could be a useful diagnostic tool. The objective of this study was to identify potential biomarkers in exhaled breath that remain stable and consistent during different physiological states, including rest and brief workouts, intending to develop a non-invasive diagnostic tool for detecting abnormal liver function. Method Our study employed a gas chromatography and mass-spectrometer quantified dataset for analysis. Machine learning techniques, including feature selection and model training, were used to rank and evaluate potential biomarkers' contributions to the model's performance. Statistical methods were applied to filter significant and consistent biomarkers. The final selected biomarkers were iterated for all possible combinations using machine learning algorithms to determine their accuracy range. Furthermore, classification models were used to evaluate the performance metrics of the biomarkers and compare models. Result The final selected biomarkers, including 2-Myristynoyl Pantetheine, Pterin-6 Carboxylic Acid, Methyl Mercaptan, N-Acetyl Cysteine, and Butyric Acid, exhibited stable levels in exhaled breath during different physiological states. They showed high accuracy and precision in detecting abnormal liver function. Our machine learning models achieved an accuracy rate ranging from 0.7 to 0.95 in all conditions, with precision, recall, prediction probability, and a 95% confidence interval ranging from 0.84 to 0.94, using various combinations of these biomarkers. Conclusion Our statistical and machine learning analysis identified significant and potential biomarkers that contribute to the detection of abnormal liver function. These biomarkers were consistent across different physiological states of the body in both patient and healthy groups. The use of breath samples and feature selection machine learning methods proved to be an accurate and reliable approach for identifying these biomarkers. Our findings provide valuable insights for future research in this field and can inform the development of non-invasive and cost-effective diagnostic tests for liver disease.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590141","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}