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Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data.

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Title: Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data.
Authors: Jujjavarapu C; Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA., Suri P; Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.; Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA., Pejaver V; Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA., Friedly J; Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.; Department of Rehabilitation Medicine, University of Washington, 1959 NE Pacific St, Seattle, WA, 98195, USA., Gold LS; Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.; Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA., Meier E; Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA.; Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA.; Center for Biomedical Statistics, University of Washington, Seattle, WA, USA., Cohen T; Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA., Mooney SD; Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Box 358047, Seattle, WA, 98195, USA., Heagerty PJ; Department of Biostatistics, University of Washington, Box 357232, Seattle, WA, 98195-7232, USA.; Center for Biomedical Statistics, University of Washington, Seattle, WA, USA., Jarvik JG; Clinical Learning, Evidence and Research Center, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98105, USA. jarvikj@uw.edu.; Department of Radiology, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA. jarvikj@uw.edu.; Department of Neurological Surgery, University of Washington, 1959 NE Pacific Street, Seattle, WA, 98195, USA. jarvikj@uw.edu.; Department of Health Services, University of Washington, Box 357660, Seattle, WA, 98195-7660, USA. jarvikj@uw.edu.
Source: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2023 Jan 06; Vol. 23 (1), pp. 2. Date of Electronic Publication: 2023 Jan 06.
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
Database: MEDLINE Ultimate
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