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

ABSTRACT:

Background

Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS.

Materials and method

We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months af

SUBMITTER: Jujjavarapu C 

PROVIDER: S-EPMC9824905 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature