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Prognosticating Lumbar Spinal Surgery Outcomes for Low Back Pain and Sciatica Patients by Utilizing Preoperative Assessments from Western and Eastern Medicine and Multimodal Fusion Learning Techniques

Published: 09 September 2024 Publication History

Abstract

Lumbar spinal surgery cannot ensure the complete alleviation of symptoms in patients suffering from low back pain (LBP) and sciatica, and there exists no efficacious means to prospectively assess surgical outcomes. By integrating assessments from both Western and Eastern medical paradigms, this study elucidated strategies for the integration of diverse modalities, encompassing tabular, textual, and auditory data, employing the methodology of multimodal fusion learning. Various fusion methodologies, including early fusion, joint fusion, and late fusion, along with techniques such as deep neural networks and tree-based algorithms, underwent comprehensive examinations to ascertain the most optimal combination. The results demonstrated that utilizing surgical plans through linguistically pretrained models yields superior outcomes, and their conjunction with tabular assessments from Western and Eastern medicine offers an magnified effect. The integration of triple modalities necessitates joint fusion to distill individual inherent information.

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  1. Prognosticating Lumbar Spinal Surgery Outcomes for Low Back Pain and Sciatica Patients by Utilizing Preoperative Assessments from Western and Eastern Medicine and Multimodal Fusion Learning Techniques

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    ICMHI '24: Proceedings of the 2024 8th International Conference on Medical and Health Informatics
    May 2024
    349 pages
    ISBN:9798400716874
    DOI:10.1145/3673971
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 09 September 2024

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