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Wyszukujesz frazę ""MACHINE learning"" wg kryterium: Temat


Tytuł :
Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events.
Autorzy :
Moghadam MC
Masoumi E
Bagherzadeh N
Ramsingh D
Kain ZN
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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] 2020 Jul; Vol. 2020, pp. 5468-5471.
Typ publikacji :
Journal Article
MeSH Terms :
Hypotension*/diagnosis
Supervised Machine Learning*
Algorithms ; Humans ; Logistic Models ; Machine Learning
Czasopismo naukowe
Tytuł :
Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure.
Autorzy :
Li P
Laleg-Kirati TM
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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] 2020 Jul; Vol. 2020, pp. 2683-2686.
Typ publikacji :
Journal Article
MeSH Terms :
Arterial Pressure*
Machine Learning*
Algorithms ; Databases, Factual ; Humans ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Vulnerability of classifiers to evolutionary generated adversarial examples.
Autorzy :
Vidnerová P; The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 271/2, 182 07 Prague 8, Czechia. Electronic address: .
Neruda R; The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 271/2, 182 07 Prague 8, Czechia. Electronic address: .
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2020 Jul; Vol. 127, pp. 168-181. Date of Electronic Publication: 2020 Apr 20.
Typ publikacji :
Journal Article
MeSH Terms :
Neural Networks, Computer*
Supervised Machine Learning*/trends
Pattern Recognition, Automated/*methods
Algorithms ; Humans ; Machine Learning/trends ; Pattern Recognition, Automated/trends
Czasopismo naukowe
Tytuł :
ColocML: machine learning quantifies co-localization between mass spectrometry images.
Autorzy :
Ovchinnikova K; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Stuart L; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Rakhlin A; Neuromation OU, Tallinn, Estonia.
Nikolenko S; National Research Institute Higher School of Economics.; Steklov Institute of Mathematics at St. Petersburg, St. Petersburg, Russia.
Alexandrov T; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.; Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany.; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA.
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Źródło :
Bioinformatics (Oxford, England) [Bioinformatics] 2020 May 01; Vol. 36 (10), pp. 3215-3224.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Neural Networks, Computer*
Mass Spectrometry ; Software ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
Autorzy :
Smith AM; Unlearn.AI, Inc., San Francisco, CA, USA. .
Walsh JR; Unlearn.AI, Inc., San Francisco, CA, USA.
Long J; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Davis CB; Oncology Global Product Development, Pfizer Inc., San Diego, CA, USA.
Henstock P; Business Technology, Pfizer Inc., Cambridge, MA, USA.
Hodge MR; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Maciejewski M; Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Mu XJ; Oncology Research & Development, Worldwide Research & Development, Pfizer Inc., San Diego, CA, USA.
Ra S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Zhao S; Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA.
Ziemek D; Inflammation and Immunology, Worldwide Research & Development, Pfizer Pharma GmbH., Berlin, Germany.
Fisher CK; Unlearn.AI, Inc., San Francisco, CA, USA.
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2020 Mar 20; Vol. 21 (1), pp. 119. Date of Electronic Publication: 2020 Mar 20.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Gene Expression Profiling*
Machine Learning*
Phenotype*
Disease/genetics ; Humans ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.
Autorzy :
Roy S; International Centre for Genetic Engineering and Biotechnology, New Delhi, India.
Kumar R; International Centre for Genetic Engineering and Biotechnology, New Delhi, India.
Mittal V; International Centre for Genetic Engineering and Biotechnology, New Delhi, India.
Gupta D; International Centre for Genetic Engineering and Biotechnology, New Delhi, India. .
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Źródło :
Scientific reports [Sci Rep] 2020 Mar 05; Vol. 10 (1), pp. 4113. Date of Electronic Publication: 2020 Mar 05.
Typ publikacji :
Evaluation Study; Journal Article
MeSH Terms :
Supervised Machine Learning*
Transcriptome*
Breast Neoplasms/*classification
Carcinoma, Ductal, Breast/*classification
Algorithms ; Breast Neoplasms/genetics ; Carcinoma, Ductal, Breast/genetics ; Databases, Genetic ; Datasets as Topic ; Early Detection of Cancer ; Female ; Gene Ontology ; Humans ; Machine Learning ; Microarray Analysis ; Models, Biological ; Neoplasm Staging ; Protein Interaction Maps ; RNA, Neoplasm ; RNA-Seq ; Reproducibility of Results
Czasopismo naukowe
Tytuł :
Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning.
Autorzy :
Lin WC; Departments of Medical Informatics and Clinical Epidemiology and.
Goldstein IH; Ophthalmology, OHSU.
Hribar MR; Departments of Medical Informatics and Clinical Epidemiology and.
Sanders DS; Ophthalmology, OHSU.; Legacy Devers Eye Institute, Portland, OR.
Chiang MF; Departments of Medical Informatics and Clinical Epidemiology and.; Ophthalmology, OHSU.
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Źródło :
AMIA ... Annual Symposium proceedings. AMIA Symposium [AMIA Annu Symp Proc] 2020 Mar 04; Vol. 2019, pp. 1121-1128. Date of Electronic Publication: 2020 Mar 04 (Print Publication: 2019).
Typ publikacji :
Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
MeSH Terms :
Ambulatory Care Facilities*/organization & administration
Machine Learning*
Ophthalmology*
Humans ; Linear Models ; Models, Statistical ; Patient Satisfaction ; Pediatrics ; ROC Curve ; Supervised Machine Learning ; Time Factors ; Time-to-Treatment
Czasopismo naukowe
Tytuł :
Machine learning for syndromic surveillance using veterinary necropsy reports.
Autorzy :
Bollig N; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.; Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI, United States of America.
Clarke L; Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, WI, United States of America.
Elsmo E; Wisconsin Veterinary Diagnostic Laboratory, University of Wisconsin-Madison, Madison, WI, United States of America.
Craven M; Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States of America.
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Źródło :
PloS one [PLoS One] 2020 Feb 05; Vol. 15 (2), pp. e0228105. Date of Electronic Publication: 2020 Feb 05 (Print Publication: 2020).
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
MeSH Terms :
Machine Learning*
Gastrointestinal Diseases/*pathology
Lung Diseases/*pathology
Urologic Diseases/*pathology
Animals ; Area Under Curve ; Deep Learning ; Logistic Models ; ROC Curve ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Machine learning in cardiovascular magnetic resonance: basic concepts and applications.
Autorzy :
Leiner T; Department of Radiology | E.01.132, Utrecht University Medical Center, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands. .
Rueckert D; Biomedical Image Analysis Group, Department of Computing, Imperial College, London, UK.
Suinesiaputra A; Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
Baeßler B; Department of Radiology, University Hospital of Cologne, Cologne, Germany.; Institute of Clinical Radiology and Nuclear Medicine, University Medical Centre Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Nezafat R; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA.
Išgum I; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.
Young AA; Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.; Department of Biomedical Engineering, King's College London, London, UK.
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Źródło :
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance [J Cardiovasc Magn Reson] 2019 Oct 07; Vol. 21 (1), pp. 61. Date of Electronic Publication: 2019 Oct 07.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Review
MeSH Terms :
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Machine Learning*
Magnetic Resonance Imaging, Cine*
Myocardial Perfusion Imaging*
Cardiovascular Diseases/*diagnostic imaging
Cardiovascular Diseases/pathology ; Cardiovascular Diseases/physiopathology ; Coronary Circulation ; Deep Learning ; Humans ; Myocardium/pathology ; Predictive Value of Tests ; Reproducibility of Results ; Supervised Machine Learning ; Unsupervised Machine Learning
Czasopismo naukowe
Tytuł :
Predicting the consequences of accidents involving dangerous substances using machine learning.
Autorzy :
Chebila M; LRPI - Institute of Health and Safety, University of Batna 2, Algeria. Electronic address: .
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Źródło :
Ecotoxicology and environmental safety [Ecotoxicol Environ Saf] 2021 Jan 15; Vol. 208, pp. 111470. Date of Electronic Publication: 2020 Oct 19.
Typ publikacji :
Journal Article
MeSH Terms :
Chemical Hazard Release*
Machine Learning*
Accidents ; Algorithms ; Bayes Theorem ; Forecasting ; Humans ; Logistic Models ; Neural Networks, Computer ; Support Vector Machine
Czasopismo naukowe
Tytuł :
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
Autorzy :
Xu M; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.; Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC, United States.
Ouyang L; Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Han L; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Sun K; Department of Emergency Medicine, The First Hospital of Nanjing Medical University, Nanjing, China.
Yu T; Department of Medical Genetics, School of Basic Medical Science Jiangsu Key Laboratory of Xenotransplantation, Nanjing Medical University, Nanjing, China.
Li Q; Department of Pediatrics, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
Tian H; Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Safarnejad L; School of Medicine, Stanford University, Stanford, CA, United States.; Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC, United States.
Zhang H; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Gao Y; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Bao FS; Department of Computer Science, Iowa State University, Ames, IA, United States.
Chen Y; Institute of HIV/AIDS/STI Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Robinson P; Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC, United States.
Ge Y; Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC, United States.
Zhu B; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Liu J; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Chen S; Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC, United States.; School of Data Science, University of North Carolina Charlotte, Charlotte, NC, United States.
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Źródło :
Journal of medical Internet research [J Med Internet Res] 2021 Jan 06; Vol. 23 (1), pp. e25535. Date of Electronic Publication: 2021 Jan 06.
Typ publikacji :
Journal Article
MeSH Terms :
Decision Support Systems, Clinical*
Health*
Machine Learning*
COVID-19/*diagnosis
Pneumonia, Viral/*diagnosis
COVID-19/diagnostic imaging ; Diagnosis, Differential ; Humans ; Middle Aged ; Pneumonia, Viral/diagnostic imaging ; SARS-CoV-2 ; Support Vector Machine ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
A Novel Machine Learning Framework for Comparison of Viral COVID-19-Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis.
Autorzy :
Chen S; Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.; School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, United States.
Zhou L; School of Business, University of North Carolina at Charlotte, Charlotte, NC, United States.
Song Y; Department of Journalism, Hong Kong Baptist University, Hong Kong, Hong Kong.
Xu Q; School of Communications, Elon University, Elon, NC, United States.
Wang P; Department of Medical Informatics, School of Public Health, Jilin University, Jilin, China.
Wang K; School of Business, University of North Carolina at Charlotte, Charlotte, NC, United States.
Ge Y; Department of Software and Information System, University of North Carolina at Charlotte, Charlotte, NC, United States.
Janies D; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, United States.
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Źródło :
Journal of medical Internet research [J Med Internet Res] 2021 Jan 06; Vol. 23 (1), pp. e24889. Date of Electronic Publication: 2021 Jan 06.
Typ publikacji :
Comparative Study; Journal Article
MeSH Terms :
COVID-19*/epidemiology
COVID-19*/virology
Health Communication*
Health Policy*
Machine Learning*
Politics*
Workflow*
Social Media/*statistics & numerical data
Cluster Analysis ; Humans ; Pandemics ; SARS-CoV-2
Czasopismo naukowe
Tytuł :
Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.
Autorzy :
Tarazona A; Department of Microbiology and Ecology, University of Valencia, Valencia, Spain.
Mateo EM; Department of Microbiology and Ecology, University of Valencia, Valencia, Spain.
Gómez JV; Department of Microbiology and Ecology, University of Valencia, Valencia, Spain.
Gavara R; Packaging Lab, Institute of Agrochemistry and Food Technology, CSIC, Av. Agustín Escardino, No 7, 46980 Paterna, Valencia, Spain.
Jiménez M; Department of Microbiology and Ecology, University of Valencia, Valencia, Spain.
Mateo F; Department of Electronic Engineering, ETSE, University of Valencia, Valencia, Spain. Electronic address: .
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Źródło :
International journal of food microbiology [Int J Food Microbiol] 2021 Jan 02; Vol. 338, pp. 109012. Date of Electronic Publication: 2020 Dec 03.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Food Microbiology/*methods
Fusarium/*drug effects
Fusarium/*metabolism
Mycotoxins/*analysis
Oils, Volatile/*pharmacology
Polyvinyls/*chemistry
Antifungal Agents/pharmacology ; Fusarium/growth & development ; Mycotoxins/biosynthesis
SCR Organism :
Fusarium culmorum
Czasopismo naukowe
Tytuł :
Using Machine Learning to Make Predictions in Patients Who Fall.
Autorzy :
Young AJ; Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: .
Hare A; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Subramanian M; Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Weaver JL; Division of Trauma, Surgical Critical Care, Burns, and Acute Care Surgery, Department of Surgery, University of California San Diego Health, San Diego, California.
Kaufman E; Division of Traumatology, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Sims C; Division of Trauma, Critical Care, and Burn, Department of Surgery, The Ohio State University, Columbus, Ohio.
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Źródło :
The Journal of surgical research [J Surg Res] 2021 Jan; Vol. 257, pp. 118-127. Date of Electronic Publication: 2020 Aug 18.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Accidental Falls/*mortality
Accidental Falls/*statistics & numerical data
Adult ; Aged ; Aged, 80 and over ; Algorithms ; Decision Trees ; Female ; Glasgow Coma Scale ; Humans ; Injury Severity Score ; Intensive Care Units ; Length of Stay ; Male ; Middle Aged ; Patient Discharge/statistics & numerical data ; ROC Curve ; Retrospective Studies ; Trauma Centers
Czasopismo naukowe
Tytuł :
Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users' perceptions.
Autorzy :
Gupta M; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Bansal A; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Indian Institute of Technology Delhi, New Delhi, Delhi, India.
Jain B; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA.
Rochelle J; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Northwestern University, Evanston, IL, USA.
Oak A; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA.
Jalali MS; MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: .
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Źródło :
International journal of medical informatics [Int J Med Inform] 2021 Jan; Vol. 145, pp. 104340. Date of Electronic Publication: 2020 Nov 10.
Typ publikacji :
Journal Article
MeSH Terms :
COVID-19*
Machine Learning*
Social Media*
Weather*
Humans ; Pandemics ; SARS-CoV-2
Czasopismo naukowe
Tytuł :
Replacing the internal standard to estimate micropollutants using deep and machine learning.
Autorzy :
Baek SS; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Choi Y; Graduate School of FEED of Eco-Friendly Offshore Structure, Changwon National University, Changwon, Gyeongsangnamdo, 51140, Republic of Korea.
Jeon J; School of Civil, Environmental and Chemical Engineering, Changwon National University, Changwon, Gyeongsangnamdo, 51140, Korea.
Pyo J; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
Park J; School of Civil, Environmental and Chemical Engineering, Changwon National University, Changwon, Gyeongsangnamdo, 51140, Korea. Electronic address: .
Cho KH; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea. Electronic address: .
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Źródło :
Water research [Water Res] 2021 Jan 01; Vol. 188, pp. 116535. Date of Electronic Publication: 2020 Oct 19.
Typ publikacji :
Journal Article
MeSH Terms :
Machine Learning*
Neural Networks, Computer*
Humans ; Isotopes ; Reference Standards ; Support Vector Machine
Czasopismo naukowe
Tytuł :
Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.
Autorzy :
Melstrom LG; Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.
Rodin AS; Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, USA.
Rossi LA; Applied AI and Data Science Department, City of Hope National Medical Center, Duarte, California, USA.
Fu P Jr; Department of Pediatrics, City of Hope National Medical Center, Duarte, California, USA.
Fong Y; Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.
Sun V; Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.; Department of Population Sciences, City of Hope National Medical Center, Duarte, California, USA.
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Źródło :
Journal of surgical oncology [J Surg Oncol] 2021 Jan; Vol. 123 (1), pp. 52-60. Date of Electronic Publication: 2020 Sep 24.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Artificial Intelligence*
Machine Learning*
Patient Generated Health Data*
Patient Reported Outcome Measures*
Surgical Oncology*
Electronic Health Records/*statistics & numerical data
Neoplasms/*surgery
Humans ; Neoplasms/pathology
Czasopismo naukowe
Tytuł :
Evolution of drug resistance in HIV protease.
Autorzy :
Shah D; Department of Computer Science, 25 Park Place, Atlanta, GA, 30303, USA.
Freas C; Department of Computer Science, 25 Park Place, Atlanta, GA, 30303, USA.
Weber IT; Department of Biology, 100 Piedmont Ave., Atlanta, GA, 30303, USA.
Harrison RW; Department of Computer Science, 25 Park Place, Atlanta, GA, 30303, USA. .; Department of Biology, 100 Piedmont Ave., Atlanta, GA, 30303, USA. .
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2020 Dec 30; Vol. 21 (Suppl 18), pp. 497. Date of Electronic Publication: 2020 Dec 30.
Typ publikacji :
Journal Article
MeSH Terms :
Evolution, Molecular*
Machine Learning*
Drug Resistance, Viral/*genetics
HIV Protease/*genetics
Genotype ; HIV Infections/drug therapy ; HIV Infections/genetics ; HIV Infections/pathology ; HIV Infections/virology ; HIV Protease Inhibitors/therapeutic use ; HIV-1/enzymology ; HIV-1/genetics ; Humans ; Phenotype
Czasopismo naukowe

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