Healthcare systems worldwide generate vast amounts of data from many different sources.
Although of high complexity for a human being, it is essential to determine the patterns and …

Diagnostic and prognostic models are increasingly important in medicine and inform many
clinical decisions. Recently, machine learning approaches have shown improvement over …

Clinical prediction models for spine surgery applications are on the rise, with an increasing
reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes …

By predicting short-term postoperative outcomes before surgery, patients who undergo
posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that …

Purpose Postoperative complication prediction helps surgeons to inform and manage
patient expectations. Deep learning, a model that finds patterns in large samples of data …

Study Design. A retrospective, case-control study. Objective. We aim to build a risk calculator
predicting major perioperative complications after anterior cervical fusion. In addition, we …

Study Design: A retrospective cohort. Objective: We utilize big data and modeling
techniques to create optimized comorbidity indices for predicting postoperative outcomes …

BACKGROUND CONTEXT Mortality in patients with spinal epidural abscess (SEA) remains
high. Accurate prediction of patient-specific prognosis in SEA can improve patient …

Background Stroke-associated pneumonia (SAP) contributes to high mortality rates in
spontaneous intracerebral hemorrhage (sICH) populations. Accurate prediction and early …

While spine surgery has historically been performed in the inpatient setting, in recent years
there has been growing interest in performing certain cervical and lumbar spine procedures …