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PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Lippincott Williams & Wilkins
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430043/ http://dx.doi.org/10.1097/01.HS9.0000973924.03429.74 |
| _version_ | 1785090859099750400 |
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| author | Thielecke, Lars Uschner, Friedemann Schadt, Jonas Roehnert, Maximilian Oelschlaegel, Uta Roeder, Ingo Bornhauser, Martin von Bonin, Malte Glauche, Ingmar |
| author_facet | Thielecke, Lars Uschner, Friedemann Schadt, Jonas Roehnert, Maximilian Oelschlaegel, Uta Roeder, Ingo Bornhauser, Martin von Bonin, Malte Glauche, Ingmar |
| author_sort | Thielecke, Lars |
| collection | PubMed |
| description | |
| format | Online Article Text |
| id | pubmed-10430043 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | Lippincott Williams & Wilkins |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-104300432023-08-17 PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA Thielecke, Lars Uschner, Friedemann Schadt, Jonas Roehnert, Maximilian Oelschlaegel, Uta Roeder, Ingo Bornhauser, Martin von Bonin, Malte Glauche, Ingmar Hemasphere Publication Only Lippincott Williams & Wilkins 2023-08-08 /pmc/articles/PMC10430043/ http://dx.doi.org/10.1097/01.HS9.0000973924.03429.74 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access Abstract Book distributed under the Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) which allows third parties to download the articles and share them with others as long as they credit the author and the Abstract Book, but they cannot change the content in any way or use them commercially. |
| spellingShingle | Publication Only Thielecke, Lars Uschner, Friedemann Schadt, Jonas Roehnert, Maximilian Oelschlaegel, Uta Roeder, Ingo Bornhauser, Martin von Bonin, Malte Glauche, Ingmar PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title | PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title_full | PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title_fullStr | PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title_full_unstemmed | PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title_short | PB1767: CHALLENGES IN MACHINE LEARNING-BASED DETECTION OF MEASURABLE RESIDUAL DISEASE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA |
| title_sort | pb1767: challenges in machine learning-based detection of measurable residual disease in patients with acute myeloid leukemia |
| topic | Publication Only |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10430043/ http://dx.doi.org/10.1097/01.HS9.0000973924.03429.74 |
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