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Littérature scientifique sur le sujet « Medical machine »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Medical machine"

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C, Liam. "Enhancing Diagnostic: Machine Learning in Medical Image Analysis." International Journal of Research Publication and Reviews 5, no. 5 (2024): 13013–16. http://dx.doi.org/10.55248/gengpi.5.0524.1458.

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Sellam, Abdelkrim, Boubakeur Dehiba, Mohamed B. Benabdallah, et al. "Vectorial Formalism of Polyphase Synchronous Machine With Permanents Magnets." Aceh International Journal of Science and Technology 2, no. 1 (2013): 1–7. http://dx.doi.org/10.13170/aijst.2.1.482.

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Abstract- This paper presents a mathematical model that transforms the real machine to fictitious machines and our goal is to simulate these and see the behavior of these machines in load. The polyphase machines are developed mainly in the field of variable speed drives of high power because increasing the number of phases on the one hand allows to reduce the dimensions of the components in power modulators energy and secondly to improve the operating safety. By a vector approach (vector space), it is possible to find a set of single-phase machine and / or two-phase fictitious equivalent to po

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Chetan, Bulla, Parushetti Chinmay, Teli Akshata, Aski Samiksha, and Koppad Sachin. "A Review of AI Based Medical Assistant Chatbot." Research and Applications of Web Development and Design 3, no. 2 (2020): 1–14. https://doi.org/10.5281/zenodo.3902215.

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<em>This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a program which allows the user to start a conversation with the machine. This is a platform focused on Artificial Intelligence (AI), which can be developed as messaging applications, web applications, or smartphone applications. A chatbot represents machine that answers ques

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Cartes-Velásquez, Ricardo. "Machine Learning and Medical Diagnosis." International Journal of Medical and Surgical Sciences 6, no. 4 (2019): 105–6. http://dx.doi.org/10.32457/ijmss.2019.031.

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Kiruba, J., R. Visalakshi, A. Vaishnavi, R. Ahalya, and RA Keerthi. "Medical Diagnosis using Machine Learning." Indian Journal of Public Health Research & Development 10, no. 4 (2019): 1337. http://dx.doi.org/10.5958/0976-5506.2019.00898.2.

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Sailaja, M., Abdul Ahad, K. Sivaramakrishna, and Ali Hussain. "Machine Learning Medical Resource Allocation." Journal of Physics: Conference Series 2089, no. 1 (2021): 012082. http://dx.doi.org/10.1088/1742-6596/2089/1/012082.

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Abstract In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes

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Wang, Qian, Yinghuan Shi, and Dinggang Shen. "Machine Learning in Medical Imaging." IEEE Journal of Biomedical and Health Informatics 23, no. 4 (2019): 1361–62. http://dx.doi.org/10.1109/jbhi.2019.2920801.

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Suzuki, Kenji, Pingkun Yan, Fei Wang, and Dinggang Shen. "Machine Learning in Medical Imaging." International Journal of Biomedical Imaging 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/123727.

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Fu, Geng-Shen, Yuri Levin-Schwartz, Qiu-Hua Lin, and Da Zhang. "Machine Learning for Medical Imaging." Journal of Healthcare Engineering 2019 (April 28, 2019): 1–2. http://dx.doi.org/10.1155/2019/9874591.

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Dolgin, Elie. "Medical devices: Managed by machine." Nature 485, no. 7398 (2012): S6—S8. http://dx.doi.org/10.1038/485s6a.

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Thèses sur le sujet "Medical machine"

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Veropoulos, Konstantinos. "Machine learning approaches to medical decision making." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367661.

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Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.

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<p> The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-proc

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Chi, Chih-Lin Street William N. "Medical decision support systems based on machine learning." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/283.

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Chi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.

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This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical resources, and improvement of outcomes. The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in t

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Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine a

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Letzner, Josefine. "Analysis of Emergency Medical Transport Datasets using Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215162.

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The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machinelearning approach for predicting hospital selection. This is a multi-classclassification problem. The performance of random forest, logistic regression and neural network were compared to each other and to a baseline, namely the one rule-algorithm. The algorithms were applied to real world data from SOS-alarm, the company that operate Sweden’s emergency call services. Performance was measured with accuracy and f1-score. Random Forest got the b

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Rosén, Henrik. "Automation of Medical Underwriting by Appliance of Machine Learning." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171843.

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One of the most important fields regarding growth and development for mostorganizations today is the digitalization, or digital transformation. The offering oftechnological solutions to enhance existing, or create new, processes or products isemerging. That is, it’s of great importance that organizations continuously affirm thepotential of applying new technical solutions into their existing processes. For example, a well implemented AI solution for automation of an existing process is likely tocontribute with considerable business value.Medical underwriting for individual insurances, which is

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Afnouch, Marwa. "Machine Learning Applications in Medical Diagnosis, case study : bone metastasis." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0039.

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Les métastases sont un groupe de cellules anormales qui se développe en dehors des limites de l’organe d’origine et se propagent à d’autres organes. En particulier, les métastases osseuses proviennent d’un organe du corps, tel que le sein, le poumon ou la prostate, et se propagent à l’os. Bien que cette maladie ait été découverte il y a plus d’un siècle, elle n’est toujours pas bien définie et les traitements existants sont peu efficaces, probablement parce qu’elle est difficile et longue à détecter. Pour aider les médecins, les nouvelles techniques d’apprentissage automatique sont une solutio

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Saleem, Afsah. "Machine learning for computer-aided diagnostics from complex medical images." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2025. https://ro.ecu.edu.au/theses/2927.

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Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorp

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Bao, Guoqing. "End-to-End Machine Learning Models for Multimodal Medical Data Analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28153.

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The pathogenesis of infectious and severe diseases including COVID-19, metabolic disorders, and cancer can be highly complicated because it involves abnormalities in genetic, metabolic, anatomical as well as functional levels. The deteriorative changes could be quantitatively monitored on biochemical markers, genome-wide assays as well as different imaging modalities including radiographic and pathological data. Multimodal medical data, involving three common and essential diagnostic disciplines, i.e., pathology, radiography, and genomics, are increasingly utilized to unravel the complexity of

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Livres sur le sujet "Medical machine"

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van Rysewyk, Simon Peter, and Matthijs Pontier, eds. Machine Medical Ethics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08108-3.

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Lian, Chunfeng, Xiaohuan Cao, Islem Rekik, Xuanang Xu, and Pingkun Yan, eds. Machine Learning in Medical Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3.

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Shi, Yinghuan, Heung-Il Suk, and Mingxia Liu, eds. Machine Learning in Medical Imaging. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00919-9.

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Bhatia, Kanwal, and Herve Lombaert, eds. Machine Learning Meets Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27929-9.

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Wang, Li, Ehsan Adeli, Qian Wang, Yinghuan Shi, and Heung-Il Suk, eds. Machine Learning in Medical Imaging. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47157-0.

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Suzuki, Kenji, Fei Wang, Dinggang Shen, and Pingkun Yan, eds. Machine Learning in Medical Imaging. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24319-6.

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Wang, Qian, Yinghuan Shi, Heung-Il Suk, and Kenji Suzuki, eds. Machine Learning in Medical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67389-9.

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Liu, Mingxia, Pingkun Yan, Chunfeng Lian, and Xiaohuan Cao, eds. Machine Learning in Medical Imaging. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7.

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Wang, Fei, Pingkun Yan, Kenji Suzuki, and Dinggang Shen, eds. Machine Learning in Medical Imaging. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15948-0.

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Wang, Fei, Dinggang Shen, Pingkun Yan, and Kenji Suzuki, eds. Machine Learning in Medical Imaging. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35428-1.

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Chapitres de livres sur le sujet "Medical machine"

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Birkfellner, Wolfgang, and Sepideh Hatamikia. "Machine Learning." In Applied Medical Image Processing, 3rd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003226154-11.

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Nisha Angeline, C. V., S. Muthuramlingam, E. Rahul Ganesh, S. Siva Pratheep, and V. Nishanthan. "Medical IoT—Automatic Medical Dispensing Machine." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0289-4_24.

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Kumar Singh, Bikesh, and G. R. Sinha. "Medical Image Processing." In Machine Learning in Healthcare. CRC Press, 2022. http://dx.doi.org/10.1201/9781003097808-4.

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Kochetkova, Tatjana. "An Overview of Machine Medical Ethics." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_1.

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Gunkel, David J. "The Rights of Machines: Caring for Robotic Care-Givers." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_10.

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van den Hoven van Genderen, Rob. "Machine Medical Ethics and Robot Law: Legal Necessity or Science Fiction?" In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_11.

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Inthorn, Julia, Marco Elio Tabacchi, and Rudolf Seising. "Having the Final Say: Machine Support of Ethical Decisions of Doctors." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_12.

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Tonkens, Ryan. "Ethics of Robotic Assisted Dying." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_13.

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Whitby, Blay. "Automating Medicine the Ethical Way." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_14.

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Hoorn, Johan F. "Machine Medical Ethics: When a Human Is Delusive but the Machine Has Its Wits About Him." In Machine Medical Ethics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08108-3_15.

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Actes de conférences sur le sujet "Medical machine"

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Valegaleti, Sesha Bhargavi, G. Sankeerthi, and M. Tejaswi. "CureQ, a medical chatbot to connect to Medical Practioners/Hospitals." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007340.

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Rani, S. Jansi, N. Saranya, Raghul T. M, Siva Prasanth B, and Tharun K. "Medical Prescription Analysis Using Machine Learning." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968161.

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Rathore, Saurabh Pratap Singh, Ali Guma, Sakshi Chamoli, Rayappan Lotus, Yogendra Kumar, and Shailendra Singh Sikarwar. "Machine Learning for Medical Image Feature Extraction." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985724.

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Adiline Macriga, G., Vishal K, and Harshath K. "Medical Diagnostics Using Machine Learning-Nail Images." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11019366.

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Dülger, L. Canan, M. Kızıl, and M. Atçeter D. D. Demircioğlu. "Design and Construction of a Portable CPR Machine." In 2024 Medical Technologies Congress (TIPTEKNO). IEEE, 2024. http://dx.doi.org/10.1109/tiptekno63488.2024.10755390.

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Yepes-Calderon, Fernando, Fabian Pedregosa, Bertrand Thirion, Yalin Wang, and Natasha Lepore. "Automatic pathology classification using a single feature machine learning support - vector machines." In SPIE Medical Imaging, edited by Stephen Aylward and Lubomir M. Hadjiiski. SPIE, 2014. http://dx.doi.org/10.1117/12.2043943.

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Funk, Richard. "Advanced medical technologies (Challenger)." In 2012 IEEE Technology Time Machine (TTM). IEEE, 2012. http://dx.doi.org/10.1109/ttm.2012.6509085.

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Edwards, PJ, DLG Hill, JA Little, VAS Sahni, and DJ Hawkes. "Medical Image Registration Incorporating Deformations." In British Machine Vision Conference 1995. British Machine Vision Association, 1995. http://dx.doi.org/10.5244/c.9.69.

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Malpe, Vaishali, and Prathamesh Tugaonkar. "Machine LearningTrends in Medical Sciences." In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2018. http://dx.doi.org/10.1109/i-smac.2018.8653756.

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Malberg, Hagen. "Advanced medical technologies (panel chair)." In 2012 IEEE Technology Time Machine (TTM). IEEE, 2012. http://dx.doi.org/10.1109/ttm.2012.6509079.

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Rapports d'organisations sur le sujet "Medical machine"

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Lucas, Christine, Emily Hadley, Jason Nance, et al. Machine Learning for Medical Coding in Health Care Surveys. National Center for Health Statistics (U.S.), 2021. http://dx.doi.org/10.15620/cdc:109828.

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Morgan, John J. Human in the Loop Machine Translation of Medical Terminology. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada522200.

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Almulihi, Qasem, and Asaad Shujaa. Does Departmental Simulation and Team Training Program Reduce Medical Error and Improve Quality of Patient Care? A Systemic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.3.0006.

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Review question / Objective: This systematic review aimed to assess whether human simulations or machine stimulations programs would help to prevent medical errors and improve patient safety. Information sources: The search terms “Medical Simulation” [Mesh], “Medication Errors” [Mesh], “Patient safety” [Mesh] were implemented, to be as specific and selective as possible. We searched for all the publications in the Medline database, Web of Science, and Google Scholar from 2000 (when the idea of simulation in healthcare to prevent ME was employed for the first time by the Institute of Medicine (

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Nikiforov, Vladimir. Smart Technical Systems of Measuring Technology and Measuring Technique, integrated into the smart complexes of medical technologies including laser Gears with the elements of Artificial Intelligence and Artificial neural network as form of Machine Learning. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2120.

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Chapman, Keshavarz-Valian, and Dolbee. L52226 Quantifications of the Effect of Dust Ingestion on Turbocharger Compressor Performance. Pipeline Research Council International, Inc. (PRCI), 2003. http://dx.doi.org/10.55274/r0011359.

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Turbomachinery that operates in a field environment, such as turbochargers utilized by the natural gas transmission industry, are subjected to environmental conditions that affect machine performance. For example, dust and oil in the atmosphere can deteriorate the machinery and as such lower operating efficiencies. The purpose of this study is to determine the effect of dust ingestion on compressor performance so operators can make informed decisions on sizing filter media and overhaul timing. Better decision making will ultimately increase the performance and lower maintenance costs.

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Adam, Gaelen P., Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We p

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Beckman, Ivan. Development of alternative air filtration materials and methods of analysis. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47188.

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Development of high efficiency particulate air (HEPA) filters demonstrate an effort to mitigate dangerous aerosol hazards at the point of production. The nuclear power industry installs HEPA filters as a final line of containment of hazardous particles. An exploration of analytical, experimental, computational, and machine learning models is presented in this dissertation to advance the science of air filtration technology. This dissertation studies, develops, and analyzes alternative air filtration materials and methods of analysis that optimize filtration efficiency and reduce resistance to

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Adam, Gaelen, Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-)Automate Evidence Synthesis: A Rapid Review and Evidence Map. AHRQ, 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine2.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, with update searches on October 3, 2024, and April 3, 2025, for comparative studies, and identified older studies using the referenc

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Gates, Allison, Michelle Gates, Shannon Sim, Sarah A. Elliott, Jennifer Pillay, and Lisa Hartling. Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool. Agency for Healthcare Research and Quality (AHRQ), 2021. http://dx.doi.org/10.23970/ahrqepcmethodscreatingefficiencies.

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Background. Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We prospectively evaluated an online machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods. For 75 randomized trials published in 2017, we manually extracted and verified data for 21 unique data elements. We uploaded the randomized trials to ExaCT, an online machine learning and text mining tool, and quantified performance by evaluati

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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 sam

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