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    Wyświetlanie 1-4 z 4
    Tytuł:
    Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.
    Autorzy:
    Irvin JA; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA. .
    Kondrich AA; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
    Ko M; Department of Statistics, Stanford University, Stanford, USA.
    Rajpurkar P; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
    Haghgoo B; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
    Landon BE; Department of Healthcare Policy, Harvard Medical School, Boston, USA.; Center for Primary Care, Harvard Medical School, Boston, USA.
    Phillips RL; Center for Professionalism & Value in Health Care, American Board of Family Medicine Foundation, Lexington, USA.
    Petterson S; Robert Graham Center, American Academy of Family Physicians, Leawood, USA.
    Ng AY; Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, CA, 94305, USA.
    Basu S; Center for Primary Care, Harvard Medical School, Boston, USA.; Research and Analytics, Collective Health, San Francisco, USA.; School of Public Health, Imperial College London, London, England.
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    Źródło:
    BMC public health [BMC Public Health] 2020 May 01; Vol. 20 (1), pp. 608. Date of Electronic Publication: 2020 May 01.
    Typ publikacji:
    Journal Article
    MeSH Terms:
    Health Promotion/*economics
    Health Promotion/*statistics & numerical data
    Insurance, Health/*economics
    Insurance, Health/*statistics & numerical data
    Machine Learning/*economics
    Machine Learning/*statistics & numerical data
    Social Determinants of Health/*economics
    Social Determinants of Health/*statistics & numerical data
    Adult ; Cost-Benefit Analysis ; Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Risk Adjustment
    Czasopismo naukowe
    Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
    Tytuł:
    AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining.
    Autorzy:
    Rajpurkar P; Stanford University Department of Computer Science, Stanford, USA.
    Park A; Stanford University Department of Computer Science, Stanford, USA.
    Irvin J; Stanford University Department of Computer Science, Stanford, USA.
    Chute C; Stanford University Department of Computer Science, Stanford, USA.
    Bereket M; Stanford University Department of Computer Science, Stanford, USA.
    Mastrodicasa D; Stanford University Department of Radiology, Stanford, USA.
    Langlotz CP; Stanford University AIMI Center, Stanford, USA.
    Lungren MP; Stanford University AIMI Center, Stanford, USA.
    Ng AY; Stanford University Department of Computer Science, Stanford, USA.
    Patel BN; Stanford University Department of Radiology, Stanford, USA. .; Stanford University AIMI Center, Stanford, USA. .
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    Źródło:
    Scientific reports [Sci Rep] 2020 Mar 03; Vol. 10 (1), pp. 3958. Date of Electronic Publication: 2020 Mar 03.
    Typ publikacji:
    Journal Article; Research Support, Non-U.S. Gov't
    MeSH Terms:
    Algorithms*
    Deep Learning*
    Appendicitis/*diagnosis
    Appendicitis/*metabolism
    Adult ; Cross-Sectional Studies ; Female ; Humans ; Male ; Middle Aged
    Czasopismo naukowe
    Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
    Tytuł:
    Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
    Autorzy:
    Bien N; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Rajpurkar P; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Ball RL; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California, United States of America.
    Irvin J; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Park A; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Jones E; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Bereket M; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Patel BN; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Yeom KW; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Shpanskaya K; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Halabi S; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Zucker E; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Fanton G; Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
    Amanatullah DF; Department of Orthopedic Surgery, Stanford University, Stanford, California, United States of America.
    Beaulieu CF; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Riley GM; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Stewart RJ; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Blankenberg FG; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Larson DB; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Jones RH; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Langlotz CP; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Ng AY; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Lungren MP; Department of Radiology, Stanford University, Stanford, California, United States of America.
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    Źródło:
    PLoS medicine [PLoS Med] 2018 Nov 27; Vol. 15 (11), pp. e1002699. Date of Electronic Publication: 2018 Nov 27 (Print Publication: 2018).
    Typ publikacji:
    Journal Article; Validation Study
    MeSH Terms:
    Deep Learning*
    Anterior Cruciate Ligament Injuries/*diagnostic imaging
    Diagnosis, Computer-Assisted/*methods
    Image Interpretation, Computer-Assisted/*methods
    Knee/*diagnostic imaging
    Magnetic Resonance Imaging/*methods
    Tibial Meniscus Injuries/*diagnostic imaging
    Adult ; Automation ; Databases, Factual ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Young Adult
    Czasopismo naukowe
    Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
    Tytuł:
    Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.
    Autorzy:
    Rajpurkar P; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Irvin J; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Ball RL; Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, California, United States of America.
    Zhu K; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Yang B; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Mehta H; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Duan T; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Ding D; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Bagul A; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Langlotz CP; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Patel BN; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Yeom KW; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Shpanskaya K; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Blankenberg FG; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Seekins J; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Amrhein TJ; Department of Radiology, Duke University, Durham, North Carolina, United States of America.
    Mong DA; Department of Radiology, University of Colorado, Denver, Colorado, United States of America.
    Halabi SS; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Zucker EJ; Department of Radiology, Stanford University, Stanford, California, United States of America.
    Ng AY; Department of Computer Science, Stanford University, Stanford, California, United States of America.
    Lungren MP; Department of Radiology, Stanford University, Stanford, California, United States of America.
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    Źródło:
    PLoS medicine [PLoS Med] 2018 Nov 20; Vol. 15 (11), pp. e1002686. Date of Electronic Publication: 2018 Nov 20 (Print Publication: 2018).
    Typ publikacji:
    Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Validation Study
    MeSH Terms:
    Clinical Competence*
    Deep Learning*
    Radiologists*
    Diagnosis, Computer-Assisted/*methods
    Pneumonia/*diagnostic imaging
    Radiographic Image Interpretation, Computer-Assisted/*methods
    Radiography, Thoracic/*methods
    Humans ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies
    Czasopismo naukowe
    Pełny tekst  Link otwiera się w nowym oknie Pełny tekstSzczegóły
      Wyświetlanie 1-4 z 4

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