The Convergence of Human and Artificial Intelligence on Clinical Care - Part I
This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innova...
Պահպանված է:
| Ձևաչափ: | Online |
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| Լեզու: | անգլերեն |
| Հրապարակվել է: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Խորագրեր: | |
| Առցանց հասանելիություն: | ONIX_20220321_9783036532967_54 |
| Ցուցիչներ: |
Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869517989519294464 |
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| collection | Directory of Open Access Books |
| description | This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all. |
| format | Online |
| id | doab-20.500.12854ir-79618 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-796182024-03-30T23:22:13Z The Convergence of Human and Artificial Intelligence on Clinical Care - Part I Abedi, Vida machine learning-enabled decision support system improving diagnosis accuracy Bayesian network bariatric surgery health-related quality of life comorbidity voice change larynx cancer machine learning deep learning voice pathology classification imputation electronic health records EHR laboratory measures medical informatics inflammatory bowel disease C. difficile infection osteoarthritis complex diseases healthcare artificial intelligence interpretable machine learning explainable machine learning septic shock clinical decision support system electronic health record cerebrovascular disorders stroke SARS-CoV-2 COVID-19 cluster analysis risk factors ischemic stroke outcome prediction recurrent stroke cardiac ultrasound echocardiography portable ultrasound aneurysm surgery temporary artery occlusion clipping time artificial neural network digital imaging monocytes promonocytes and monoblasts chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia concordance between hematopathologists mechanical ventilation respiratory failure ADHD social media Twitter pharmacotherapy stimulants alpha-2-adrenergic agonists non-stimulants trust passive adherence human factors thema EDItEUR::M Medicine and Nursing This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all. 2022-03-21T16:28:19Z 2022-03-21T16:28:19Z 2022 book ONIX_20220321_9783036532967_54 9783036532967 9783036532950 https://directory.doabooks.org/handle/20.500.12854/79618 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5003 https://mdpi.com/books/pdfview/book/5003 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3295-0 10.3390/books978-3-0365-3295-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036532967 9783036532950 188 Basel open access |
| spellingShingle | machine learning-enabled decision support system improving diagnosis accuracy Bayesian network bariatric surgery health-related quality of life comorbidity voice change larynx cancer machine learning deep learning voice pathology classification imputation electronic health records EHR laboratory measures medical informatics inflammatory bowel disease C. difficile infection osteoarthritis complex diseases healthcare artificial intelligence interpretable machine learning explainable machine learning septic shock clinical decision support system electronic health record cerebrovascular disorders stroke SARS-CoV-2 COVID-19 cluster analysis risk factors ischemic stroke outcome prediction recurrent stroke cardiac ultrasound echocardiography portable ultrasound aneurysm surgery temporary artery occlusion clipping time artificial neural network digital imaging monocytes promonocytes and monoblasts chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia concordance between hematopathologists mechanical ventilation respiratory failure ADHD social media pharmacotherapy stimulants alpha-2-adrenergic agonists non-stimulants trust passive adherence human factors thema EDItEUR::M Medicine and Nursing The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title | The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title_full | The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title_fullStr | The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title_full_unstemmed | The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title_short | The Convergence of Human and Artificial Intelligence on Clinical Care - Part I |
| title_sort | convergence of human and artificial intelligence on clinical care part i |
| topic | machine learning-enabled decision support system improving diagnosis accuracy Bayesian network bariatric surgery health-related quality of life comorbidity voice change larynx cancer machine learning deep learning voice pathology classification imputation electronic health records EHR laboratory measures medical informatics inflammatory bowel disease C. difficile infection osteoarthritis complex diseases healthcare artificial intelligence interpretable machine learning explainable machine learning septic shock clinical decision support system electronic health record cerebrovascular disorders stroke SARS-CoV-2 COVID-19 cluster analysis risk factors ischemic stroke outcome prediction recurrent stroke cardiac ultrasound echocardiography portable ultrasound aneurysm surgery temporary artery occlusion clipping time artificial neural network digital imaging monocytes promonocytes and monoblasts chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia concordance between hematopathologists mechanical ventilation respiratory failure ADHD social media pharmacotherapy stimulants alpha-2-adrenergic agonists non-stimulants trust passive adherence human factors thema EDItEUR::M Medicine and Nursing |
| topic_facet | machine learning-enabled decision support system improving diagnosis accuracy Bayesian network bariatric surgery health-related quality of life comorbidity voice change larynx cancer machine learning deep learning voice pathology classification imputation electronic health records EHR laboratory measures medical informatics inflammatory bowel disease C. difficile infection osteoarthritis complex diseases healthcare artificial intelligence interpretable machine learning explainable machine learning septic shock clinical decision support system electronic health record cerebrovascular disorders stroke SARS-CoV-2 COVID-19 cluster analysis risk factors ischemic stroke outcome prediction recurrent stroke cardiac ultrasound echocardiography portable ultrasound aneurysm surgery temporary artery occlusion clipping time artificial neural network digital imaging monocytes promonocytes and monoblasts chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia concordance between hematopathologists mechanical ventilation respiratory failure ADHD social media pharmacotherapy stimulants alpha-2-adrenergic agonists non-stimulants trust passive adherence human factors thema EDItEUR::M Medicine and Nursing |
| url | ONIX_20220321_9783036532967_54 |