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Wyświetlanie 1-20 z 832017

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Podgląd okładki
Tytuł :
Deus ex machina? Demystifying rather than deifying machine learning.
Autorzy :
Domaratzki M; Department of Computer Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.
Kidane B; Section of Thoracic Surgery, Department of Surgery, University of Manitoba, Winnipeg, Manitoba, Canada; Research Institute in Oncology and Hematology, Cancer Care Manitoba, Winnipeg, Manitoba, Canada; Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: .
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Źródło :
The Journal of thoracic and cardiovascular surgery [J Thorac Cardiovasc Surg] 2022 Mar; Vol. 163 (3), pp. 1131-1137.e4. Date of Electronic Publication: 2021 Mar 11.
Typ publikacji :
Editorial; Review
MeSH Terms :
Big Data*
Computational Biology*
Machine Learning*
Data Mining/*methods
Deep Learning ; Diffusion of Innovation ; Humans ; Supervised Machine Learning ; Unsupervised Machine Learning
Recenzja
Szczegóły
Podgląd okładki
Tytuł :
An analysis framework for clustering algorithm selection with applications to spectroscopy.
Autorzy :
Crase S; College of Engineering, IT & Environment, Charles Darwin University, Darwin, Northern Territory, Australia.; Defence Science and Technology Group, Edinburgh, South Australia, Australia.
Thennadil SN; College of Engineering, IT & Environment, Charles Darwin University, Darwin, Northern Territory, Australia.; Energy and Resources Institute, Charles Darwin University, Darwin, Northern Territory, Australia.
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Źródło :
PloS one [PLoS One] 2022 Mar 31; Vol. 17 (3), pp. e0266369. Date of Electronic Publication: 2022 Mar 31 (Print Publication: 2022).
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Algorithms*
Unsupervised Machine Learning*
Cluster Analysis ; Machine Learning ; Spectrum Analysis
Czasopismo naukowe
Szczegóły Pełny tekst Pełny tekst
Podgląd okładki
Tytuł :
Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems.
Autorzy :
Gardner W; Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia.
Winkler DA; La Trobe Institute for Molecular Sciences, La Trobe University, Bundoora, Victoria 3086, Australia.
Muir BW; CSIRO Manufacturing, Clayton, Victoria 3086, Australia.
Pigram PJ; Centre for Materials and Surface Science and Department of Chemistry and Physics, La Trobe University, Bundoora, Victoria 3086, Australia.
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Źródło :
Biointerphases [Biointerphases] 2022 Mar 28; Vol. 17 (2), pp. 020802. Date of Electronic Publication: 2022 Mar 28.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't; Review
MeSH Terms :
Spectrometry, Mass, Secondary Ion*/methods
Unsupervised Machine Learning*
Algorithms ; Machine Learning ; Multivariate Analysis
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Machine Learning Approaches for Early Prostate Cancer Prediction Based on Healthcare Utilization Patterns.
Autorzy :
Finkelstein J; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Cui W; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Martin TC; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Parsons R; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Źródło :
Studies in health technology and informatics [Stud Health Technol Inform] 2022 Jan 14; Vol. 289, pp. 65-68.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Prostatic Neoplasms*/diagnosis
Prostatic Neoplasms*/epidemiology
Prostatic Neoplasms*/therapy
Humans ; Male ; Patient Acceptance of Health Care ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
A Conceptual Framework to Predict Mental Health Patients' Zoning Classification.
Autorzy :
Pandey SR; School of Computer Science & Engineering, University of Westminster, London, UK.
Smith A; Business Intelligence Systems Team, Oxleas NHS Foundation Trust, London, UK.
Gall EN; School of Computer Science & Engineering, University of Westminster, London, UK.
Bhatnagar A; Green Parks House (Inpatient Services), Oxleas NHS Foundation Trust, London, UK.
Chaussalet T; School of Computer Science & Engineering, University of Westminster, London, UK.
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Źródło :
Studies in health technology and informatics [Stud Health Technol Inform] 2022 Jan 14; Vol. 289, pp. 321-324.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Mental Health*
Electronic Health Records ; Humans ; Natural Language Processing ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Using Machine Learning to Improve Personalised Prediction: A Data-Driven Approach to Segment and Stratify Populations for Healthcare.
Autorzy :
Yuill W; Institute of Health Informatics, University College London, UK.; Hertfordshire County Council, UK.
Kunz H; Institute of Health Informatics, University College London, UK.
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Źródło :
Studies in health technology and informatics [Stud Health Technol Inform] 2022 Jan 14; Vol. 289, pp. 29-32.
Typ publikacji :
Journal Article
MeSH Terms :
Delivery of Health Care*
Machine Learning*
Cluster Analysis ; Predictive Value of Tests ; Unsupervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Feature engineering solution with structured query language analytic functions in detecting electricity frauds using machine learning.
Autorzy :
Oprea SV; Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Romana Square 6, 010374, Bucharest, Romania. .
Bâra A; Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, Romana Square 6, 010374, Bucharest, Romania.
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Źródło :
Scientific reports [Sci Rep] 2022 Feb 28; Vol. 12 (1), pp. 3257. Date of Electronic Publication: 2022 Feb 28.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Algorithms*
Machine Learning*
Electricity ; Fraud ; Unsupervised Machine Learning
Czasopismo naukowe
Szczegóły Pełny tekst Pełny tekst
Podgląd okładki
Tytuł :
A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets.
Autorzy :
Frasier KE; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America.
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Źródło :
PLoS computational biology [PLoS Comput Biol] 2021 Dec 03; Vol. 17 (12), pp. e1009613. Date of Electronic Publication: 2021 Dec 03 (Print Publication: 2021).
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Acoustics*
Machine Learning*
Cetacea/*physiology
Echolocation/*physiology
Algorithms ; Animals ; California ; Cluster Analysis ; Computational Biology ; Data Interpretation, Statistical ; Databases, Factual ; Deep Learning ; Software Design ; Unsupervised Machine Learning ; Whales/physiology
Czasopismo naukowe
Szczegóły Pełny tekst Pełny tekst
Podgląd okładki
Tytuł :
Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology.
Autorzy :
Shoop-Worrall SJW; Centre for Health Informatics, The University of Manchester, Manchester, UK .; Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK.
Cresswell K; NIHR Manchester BRC, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK.; Vocal, Manchester University NHS Foundation Trust, Manchester, UK.
Bolger I; Your Rheum, Young Person's Research Advisory Group, Manchester, UK.
Dillon B; Your Rheum, Young Person's Research Advisory Group, Manchester, UK.
Hyrich KL; Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK.; NIHR Manchester BRC, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK.
Geifman N; Centre for Health Informatics, The University of Manchester, Manchester, UK.; Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK.
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Corporate Authors :
Members of the CLUSTER consortium
Źródło :
Annals of the rheumatic diseases [Ann Rheum Dis] 2021 Dec; Vol. 80 (12), pp. 1505-1510. Date of Electronic Publication: 2021 Jul 05.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Health Personnel*
Patient Participation*
Rheumatology*
Stakeholder Participation*
Unsupervised Machine Learning*
Humans ; Machine Learning ; Patient Outcome Assessment
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Bayesian supervised machine learning classification of neural networks with pathological perturbations.
Autorzy :
Levi R; Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy.; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.; Humanitas Clinical and Research Center-IRCCS, Rozzano, Milan, Italy.
Valderhaug VD; Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
Castelbuono S; Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy.
Sandvig A; Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.; Department of Clinical Neurosciences, Division of Neuro, Head, and Neck, Umeå University Hospital, Umeå, Sweden.; Department of Community and Rehabilitation, Division of Neuro, Head and Neck, Umeå University Hospital, Umeå, Sweden.
Sandvig I; Department of Clinical Neurosciences, Division of Neuro, Head, and Neck, Umeå University Hospital, Umeå, Sweden.
Barbieri R; Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy.
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Źródło :
Biomedical physics & engineering express [Biomed Phys Eng Express] 2021 Oct 05; Vol. 7 (6). Date of Electronic Publication: 2021 Oct 05.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Neural Networks, Computer*
Supervised Machine Learning*
Algorithms ; Bayes Theorem ; Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Phenotypic Characterization of Chronic Kidney Patients Through Hierarchical Clustering.
Autorzy :
Silva RS
Pereira CL
Melo NAC
Silva GCS
Sousa CM
Sousa NPS
Caneiro ECRL
Filho AKDB
Santana EEC
Pokaż więcej
Źródło :
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 2451-2454.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Renal Insufficiency, Chronic*/diagnosis
Cluster Analysis ; Humans ; Kidney ; Unsupervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.
Autorzy :
Zhou Z
Qian X
Hu J
Zhu J
Geng C
Dai Y
Pokaż więcej
Źródło :
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 2810-2814.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Software*
Algorithms ; Retrospective Studies ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas.
Autorzy :
Bai L
Le Bourdais F
Miorelli R
Calmon P
Velichko A
Drinkwater BW
Pokaż więcej
Źródło :
IEEE transactions on ultrasonics, ferroelectrics, and frequency control [IEEE Trans Ultrason Ferroelectr Freq Control] 2021 Oct; Vol. 68 (10), pp. 3143-3155. Date of Electronic Publication: 2021 Sep 27.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Ultrasonics*
Bayes Theorem ; Neural Networks, Computer ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis.
Autorzy :
Profant O; Department of Auditory Neuroscience, Institute of Experimental Medicine, Czech Academy of Sciences, Prague, Czech Republic.
Bureš Z; Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Jugoslávských partyzánů 1580/3, 160 00, Prague 6, Czech Republic. .
Balogová Z; Department of Otorhinolaryngology, 3rd Faculty of Medicine, University Hospital Královské Vinohrady, Charles University in Prague, Prague, Czech Republic.
Betka J; Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University in Prague, Prague, Czech Republic.
Fík Z; Department of Otorhinolaryngology and Head and Neck Surgery, 1st Faculty of Medicine, University Hospital Motol, Charles University in Prague, Prague, Czech Republic.
Chovanec M; Department of Otorhinolaryngology, 3rd Faculty of Medicine, University Hospital Královské Vinohrady, Charles University in Prague, Prague, Czech Republic.
Voráček J; Faculty of Management, Prague University of Economics and Business, Jindrichuv Hradec, Czech Republic.
Pokaż więcej
Źródło :
Scientific reports [Sci Rep] 2021 Sep 15; Vol. 11 (1), pp. 18376. Date of Electronic Publication: 2021 Sep 15.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Clinical Decision-Making*
Disease Management*
Machine Learning*
Neuroma, Acoustic/*diagnosis
Neuroma, Acoustic/*therapy
Adult ; Aged ; Decision Trees ; Female ; Hearing ; Hearing Tests ; Humans ; Male ; Middle Aged ; ROC Curve ; Reproducibility of Results ; Supervised Machine Learning ; Symptom Assessment
Czasopismo naukowe
Szczegóły Pełny tekst
Podgląd okładki
Tytuł :
Predicting the risk of cancer in adults using supervised machine learning: a scoping review.
Autorzy :
Abdullah Alfayez A; Institute of Health Informatics, University College London, London, UK .; King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Kunz H; Institute of Health Informatics, University College London, London, UK.
Grace Lai A; Institute of Health Informatics, University College London, London, UK .
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Źródło :
BMJ open [BMJ Open] 2021 Sep 14; Vol. 11 (9), pp. e047755. Date of Electronic Publication: 2021 Sep 14.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't; Review
MeSH Terms :
Machine Learning*
Neoplasms*/diagnosis
Neoplasms*/epidemiology
Adult ; Calibration ; Humans ; Risk Factors ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts.
Autorzy :
Wang R; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China.
Song Q; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China.; National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China.
Liu Z; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China.; National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China.
Ma H; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China.; National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China.
Gupta MK; Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland.
Liu Z; School of Information Science and Engineering, Shandong University, Qingdao 266237, China.
Pokaż więcej
Źródło :
Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Aug 27; Vol. 21 (17). Date of Electronic Publication: 2021 Aug 27.
Typ publikacji :
Journal Article
MeSH Terms :
Algorithms*
Unsupervised Machine Learning*
Fractals ; Machine Learning ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły Pełny tekst
Podgląd okładki
Tytuł :
Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer.
Autorzy :
Le NQK; Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan.; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan.
Kha QH; International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.
Nguyen VH; International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan.; Oncology Center, Bai Chay Hospital, Quang Ninh 20000, Vietnam.
Chen YC; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan.
Cheng SJ; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan.
Chen CY; Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan.; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan.; Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan.; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
Pokaż więcej
Źródło :
International journal of molecular sciences [Int J Mol Sci] 2021 Aug 26; Vol. 22 (17). Date of Electronic Publication: 2021 Aug 26.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Mutation*
Carcinoma, Non-Small-Cell Lung/*diagnostic imaging
Carcinoma, Non-Small-Cell Lung/*genetics
Lung Neoplasms/*diagnostic imaging
Lung Neoplasms/*genetics
Proto-Oncogene Proteins p21(ras)/*genetics
Aged ; Aged, 80 and over ; Algorithms ; Biomarkers ; Carcinoma, Non-Small-Cell Lung/pathology ; ErbB Receptors/genetics ; Female ; Humans ; Lung Neoplasms/pathology ; Male ; Middle Aged ; Neoplasm Staging ; ROC Curve ; Reproducibility of Results ; Supervised Machine Learning ; Tomography, X-Ray Computed
Czasopismo naukowe
Szczegóły Pełny tekst
Podgląd okładki
Tytuł :
Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.
Autorzy :
Achilonu OJ; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa.
Fabian J; Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.; Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.
Bebington B; Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa.; Department of Surgery, Faculty of Health Science University of the Witwatersrand Faculty of Science, Parktown, Johannesburg, South Africa.
Singh E; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa.; National Cancer Registry, National Health Laboratory Service, 1 Modderfontein Road, Sandringham, Johannesburg, South Africa.
Eijkemans MJC; Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht University, Utrecht, Netherlands.
Musenge E; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa.; Industrialization, Science, Technology and Innovation Hub, African Union Development Agency (AUDA-NEPAD), Johannesburg, South Africa.
Pokaż więcej
Źródło :
Frontiers in public health [Front Public Health] 2021 Jul 07; Vol. 9, pp. 694306. Date of Electronic Publication: 2021 Jul 07 (Print Publication: 2021).
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Colorectal Neoplasms*/diagnosis
Machine Learning*
Bayes Theorem ; Humans ; South Africa/epidemiology ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły
Podgląd okładki
Tytuł :
Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.
Autorzy :
Wang N; School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing, 100069, People's Republic of China.; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, People's Republic of China.
Huang Y; School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing, 100069, People's Republic of China.; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, People's Republic of China.
Liu H; School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing, 100069, People's Republic of China.; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, People's Republic of China.
Zhang Z; School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing, 100069, People's Republic of China.; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, People's Republic of China.
Wei L; Information Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China.
Fei X; Information Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, People's Republic of China.
Chen H; School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao, You An Men, Fengtai District, Beijing, 100069, People's Republic of China. .; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, 100069, People's Republic of China. .
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Źródło :
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2021 Jul 30; Vol. 21 (Suppl 2), pp. 58. Date of Electronic Publication: 2021 Jul 30.
Typ publikacji :
Journal Article; Randomized Controlled Trial; Research Support, Non-U.S. Gov't
MeSH Terms :
Electronic Health Records*
Machine Learning*
Algorithms ; Cluster Analysis ; Humans ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły Pełny tekst
Podgląd okładki
Tytuł :
A Small World Graph Approach for an Efficient Indoor Positioning System.
Autorzy :
Lima M; Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil.
Guimarães L; Institute of Innovation, Research, and Scientific Development of Amazonas, Manaus 69010-001, Brazil.
Santos E; Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil.
Moura E; Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil.
Costa R; Education Technologies, Positivo Technologies, Curitiba 81350-000, Brazil.
Levorato M; Computer Science Department, University of California, Irvine, CA 92697, USA.
Oliveira H; Institute of Computing, Federal University of Amazonas, Manaus 69080-900, Brazil.
Pokaż więcej
Źródło :
Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Jul 23; Vol. 21 (15). Date of Electronic Publication: 2021 Jul 23.
Typ publikacji :
Journal Article
MeSH Terms :
Algorithms*
Machine Learning*
Cluster Analysis ; Databases, Factual ; Humans ; Supervised Machine Learning
Czasopismo naukowe
Szczegóły Pełny tekst Pełny tekst
Wyświetlanie 1-20 z 832017

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