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Wyszukujesz frazę ""Machine Learning"" wg kryterium: Temat


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.
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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
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.
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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
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
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.
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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
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
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.
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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
Tytuł :
Malware detection based on semi-supervised learning with malware visualization.
Autorzy :
Gao T; School of Cyber Science and Engineering, Sichuan University, China.
Zhao L; Science and Technology on Electronic Information Control Laboratory, China.
Li X; School of Cyber Science and Engineering, Sichuan University, China.
Chen W; School of Cyber Science and Engineering, Sichuan University, China.
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Źródło :
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2021 Jul 02; Vol. 18 (5), pp. 5995-6011.
Typ publikacji :
Journal Article
MeSH Terms :
Algorithms*
Supervised Machine Learning*
Machine Learning
Czasopismo naukowe
Tytuł :
Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML.
Autorzy :
Rashidi HH; Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA.
Tran N; Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA.
Albahra S; Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA.
Dang LT; Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Sacramento, CA, USA.
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Źródło :
International journal of laboratory hematology [Int J Lab Hematol] 2021 Jul; Vol. 43 Suppl 1, pp. 15-22.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Machine Learning*
Delivery of Health Care/*methods
Medical Laboratory Science/*methods
Algorithms ; Artificial Intelligence ; Automation ; Research Design ; Supervised Machine Learning ; Workflow
Czasopismo naukowe
Tytuł :
Longitudinal self-supervised learning.
Autorzy :
Zhao Q; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.
Liu Z; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
Adeli E; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
Pohl KM; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Sciences, SRI International, Menlo Park, CA 95025, USA. Electronic address: .
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Źródło :
Medical image analysis [Med Image Anal] 2021 Jul; Vol. 71, pp. 102051. Date of Electronic Publication: 2021 Apr 04.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
MeSH Terms :
Magnetic Resonance Imaging*
Supervised Machine Learning*
Brain/diagnostic imaging ; Humans ; Machine Learning
Czasopismo naukowe
Tytuł :
Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset.
Autorzy :
Moualla S; Department of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
Khorzom K; Department of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
Jafar A; Department of Telecommunication, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
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Źródło :
Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 Jun 15; Vol. 2021, pp. 5557577. Date of Electronic Publication: 2021 Jun 15 (Print Publication: 2021).
Typ publikacji :
Journal Article
MeSH Terms :
Computer Security*
Machine Learning*
ROC Curve ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Semi-supervised random forest regression model based on co-training and grouping with information entropy for evaluation of depression symptoms severity.
Autorzy :
Lu S; Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.; The Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China.; Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China.
Shi X; Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.; The Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China.; Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China.
Li M; Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.; The Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China.; Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China.; Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
Jiao J; Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.; The Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China.; Engineering Research Center of Intelligent Perception and Autonomous Control, Ministry of Education, Beijing 100124, China.
Feng L; The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China.; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100088, China.
Wang G; The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China.; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100088, China.
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Źródło :
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2021 May 27; Vol. 18 (4), pp. 4586-4602.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Depression*/diagnosis
Supervised Machine Learning*
Algorithms ; Entropy ; Humans ; Machine Learning
Czasopismo naukowe
Tytuł :
Regression plane concept for analysing continuous cellular processes with machine learning.
Autorzy :
Szkalisity A; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary.; Department of Anatomy and Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Piccinini F; Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, FC, Italy.
Beleon A; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary.
Balassa T; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary.
Varga IG; Institute of Genetics, Biological Research Center (BRC), Szeged, Hungary.
Migh E; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary.
Molnar C; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary.
Paavolainen L; Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland.
Timonen S; Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland.
Banerjee I; Indian Institute of Science Education and Research (IISER), Mohali, India.
Ikonen E; Department of Anatomy and Stem Cells and Metabolism Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Yamauchi Y; School of Cellular and Molecular Medicine, University of Bristol, BS8 1TD University Walk, Bristol, UK.
Ando I; Institute of Genetics, Biological Research Center (BRC), Szeged, Hungary.
Peltonen J; Faculty of Information Technology and Communication Sciences, Tampere University, FI-33014 Tampere University, Tampere, Finland.; Department of Computer Science, Aalto University, Aalto, Finland.
Pietiäinen V; Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland.
Honti V; Institute of Genetics, Biological Research Center (BRC), Szeged, Hungary.
Horvath P; Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Szeged, Hungary. .; Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland. .; Single-Cell Technologies Ltd., Szeged, Hungary. .
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Źródło :
Nature communications [Nat Commun] 2021 May 05; Vol. 12 (1), pp. 2532. Date of Electronic Publication: 2021 May 05.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Biological Phenomena*
Cell Physiological Phenomena*
Machine Learning*
Animals ; Carcinoma, Hepatocellular ; Cell Cycle ; Cell Differentiation ; Cell Line, Tumor ; Drosophila melanogaster ; Humans ; Membrane Proteins ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.
Autorzy :
Kaneko H; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Umakoshi H; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan. .
Ogata M; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Wada N; Department of Diabetes and Endocrinology, Sapporo City General Hospital, Sapporo, Japan.
Iwahashi N; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Fukumoto T; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Yokomoto-Umakoshi M; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Nakano Y; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Matsuda Y; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Miyazawa T; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Sakamoto R; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.
Ogawa Y; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan. .
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Źródło :
Scientific reports [Sci Rep] 2021 May 04; Vol. 11 (1), pp. 9140. Date of Electronic Publication: 2021 May 04.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Biomarkers/*blood
Hyperaldosteronism/*blood
Hyperaldosteronism/*diagnosis
Adult ; Aged ; Blood Chemical Analysis/methods ; Disease Management ; Female ; Humans ; Logistic Models ; Male ; Middle Aged ; Prognosis ; ROC Curve ; Supervised Machine Learning ; Support Vector Machine ; Workflow
Czasopismo naukowe
Tytuł :
Domain adaptation and self-supervised learning for surgical margin detection.
Autorzy :
Santilli AML; School of Computing, Queen's University, Ontario, Canada. .
Jamzad A; School of Computing, Queen's University, Ontario, Canada.
Sedghi A; School of Computing, Queen's University, Ontario, Canada.
Kaufmann M; Department of Surgery, Queen's University, Ontario, Canada.
Logan K; Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
Wallis J; Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
Ren KYM; Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
Janssen N; School of Computing, Queen's University, Ontario, Canada.
Merchant S; Department of Surgery, Queen's University, Ontario, Canada.
Engel J; Department of Surgery, Queen's University, Ontario, Canada.
McKay D; Department of Surgery, Queen's University, Ontario, Canada.
Varma S; Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
Wang A; Department of Pathology and Molecular Medicine, Queen's University, Ontario, Canada.
Fichtinger G; School of Computing, Queen's University, Ontario, Canada.
Rudan JF; Department of Surgery, Queen's University, Ontario, Canada.
Mousavi P; School of Computing, Queen's University, Ontario, Canada.
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Źródło :
International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2021 May; Vol. 16 (5), pp. 861-869. Date of Electronic Publication: 2021 May 06.
Typ publikacji :
Journal Article
MeSH Terms :
Margins of Excision*
Supervised Machine Learning*
Breast/*surgery
Breast Neoplasms/*surgery
Mastectomy, Segmental/*methods
Skin/*diagnostic imaging
Algorithms ; Area Under Curve ; Breast Neoplasms/diagnostic imaging ; Calibration ; Carcinoma, Basal Cell/diagnostic imaging ; Female ; Humans ; Machine Learning ; Mastectomy ; Operating Rooms ; Reproducibility of Results ; Sensitivity and Specificity ; Skin Neoplasms/diagnostic imaging ; Stochastic Processes
Czasopismo naukowe
Tytuł :
Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.
Autorzy :
Fagerholm U; Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden.
Hellberg S; Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden.
Spjuth O; Prosilico AB, Lännavägen 7, SE-141 45 Huddinge, Sweden.; Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24 Uppsala, Sweden.
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Źródło :
Molecules (Basel, Switzerland) [Molecules] 2021 Apr 28; Vol. 26 (9). Date of Electronic Publication: 2021 Apr 28.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Models, Biological*
Pharmaceutical Preparations*/administration & dosage
Pharmaceutical Preparations*/chemistry
Pharmacokinetics*
Administration, Oral ; Biological Availability ; Computer Simulation ; Drug Evaluation, Preclinical ; Humans ; Quantitative Structure-Activity Relationship ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Generative transfer learning for measuring plausibility of EHR diagnosis records.
Autorzy :
Estiri H; Harvard Medical School, Boston, Massachusetts, USA.; Massachusetts General Hospital, Boston, Massachusetts, USA.; Mass General Brigham, Boston, Massachusetts, USA.
Vasey S; Department of Mathematics, Harvard University, Cambridge, Massachusetts, USA.
Murphy SN; Harvard Medical School, Boston, Massachusetts, USA.; Massachusetts General Hospital, Boston, Massachusetts, USA.; Mass General Brigham, Boston, Massachusetts, USA.
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Źródło :
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2021 Mar 01; Vol. 28 (3), pp. 559-568.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural
MeSH Terms :
Diagnosis*
Electronic Health Records*
Machine Learning*
Disease/*classification
Delivery of Health Care ; Humans ; Probability ; Professional-Patient Relations ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
A pre-training and self-training approach for biomedical named entity recognition.
Autorzy :
Gao S; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
Kotevska O; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
Sorokine A; Geospatial Science and Human Security Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
Christian JB; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
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Źródło :
PloS one [PLoS One] 2021 Feb 09; Vol. 16 (2), pp. e0246310. Date of Electronic Publication: 2021 Feb 09 (Print Publication: 2021).
Typ publikacji :
Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
MeSH Terms :
Artificial Intelligence*
Recognition, Psychology*
Unsupervised Machine Learning*
Humans ; Models, Theoretical ; Supervised Machine Learning ; Terminology as Topic ; Transfer, Psychology ; Unified Medical Language System
Czasopismo naukowe
Tytuł :
Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter.
Autorzy :
Mackey TK; Department of Anesthesiology and Division of Infectious Disease and Global Public Health, University of California, San Diego, San Diego, CA 92037, USA; Department of Healthcare Research and Policy, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA; S-3 Research, San Diego, CA, USA. Electronic address: .
Purushothaman V; Department of Family Medicine and Public Health, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA.
Haupt M; Department of Cognitive Science, University of California, San Diego, San Diego, CA 92037, USA.
Nali MC; Department of Anesthesiology and Division of Infectious Disease and Global Public Health, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA; S-3 Research, San Diego, CA, USA.
Li J; Department of Healthcare Research and Policy, University of California, San Diego, San Diego, CA 92037, USA; Global Health Policy Institute, San Diego, CA, USA; S-3 Research, San Diego, CA, USA.
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Źródło :
The Lancet. Digital health [Lancet Digit Health] 2021 Feb; Vol. 3 (2), pp. e72-e75.
Typ publikacji :
Journal Article
MeSH Terms :
Communication*
Hydroxychloroquine*
Social Media*
Unsupervised Machine Learning*
COVID-19/drug therapy ; Humans ; Internet Use ; Machine Learning ; Quackery
Czasopismo naukowe
Tytuł :
Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses.
Autorzy :
Lin Q; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Department of Brain Functioning Research, The Seventh Hospital of Hangzhou, 305 Tianmushan Road, Hangzhou, Zhejiang, China.
Huang G; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China.
Li L; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China.
Zhang L; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China.
Liang Z; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China.
Anter AM; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China.
Zhang Z; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong 518060, China; Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China. Electronic address: .
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Źródło :
NeuroImage [Neuroimage] 2021 Jan 15; Vol. 225, pp. 117506. Date of Electronic Publication: 2020 Oct 27.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Supervised Machine Learning*
Brain/*diagnostic imaging
Nociceptive Pain/*diagnostic imaging
Adult ; Cluster Analysis ; Female ; Functional Neuroimaging ; Humans ; Least-Squares Analysis ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Nociceptive Pain/physiopathology ; Pain Measurement ; Young Adult
Czasopismo naukowe
Tytuł :
Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2.
Autorzy :
Bitencourt-Ferreira G; Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil.
Duarte da Silva A; Specialization Program in Bioinformatics. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900, Brazil.
Filgueira de Azevedo W Jr; Laboratory of Computational Systems Biology. Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900 , Brazil.
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Źródło :
Current medicinal chemistry [Curr Med Chem] 2021; Vol. 28 (2), pp. 253-265.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Machine Learning*
Cyclin-Dependent Kinase 2 ; Humans ; Ligands ; Molecular Docking Simulation ; Pharmaceutical Preparations ; Protein Binding ; Supervised Machine Learning
Czasopismo naukowe

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