ArticleGlobal spine journal2022
Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data.
Article in Global spine journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of minimum important changes for generic multi-attribute utility instruments and recommendations for their estimation.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Pooled it
- Values derived from patient reported outcomes in spine surgery: a systematic review of the minimal clinically important difference, substantial clinical benefit, and patient acceptable symptom state.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Pooled it
- Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.BMC medical informatics and decision making · 2024Trial
- Article
- Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods.Health information science and systems · 2025Article
- Cross species reproducibility of MRI radiomics features enables intervertebral disc degeneration assessment in experimental monkeys.Scientific reports · 2025Article
- Machine learning can predict surgical indication: new clustering model from a large adult spine deformity database.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- External validation of a prediction model for disability and pain after lumbar disc herniation surgery: a prospective international registry-based cohort study.Acta orthopaedica · 2025Article
- Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review.Health and quality of life outcomes · 2025Article
- Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery.Bioengineering (Basel, Switzerland) · 2025Review
- Validity and accuracy of a machine learning predictive model in the exploitation of patient-related outcomes in spine surgery.Frontiers in surgery · 2025Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- The strategic use of Big Data - A study protocol for a multicenter clinical trial testing if the use of the Swespine Dialogue Support alter outcomes in degenerative spine surgery.BMC musculoskeletal disorders · 2024Article
- Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.Current reviews in musculoskeletal medicine · 2024Review
- PROPOSE. Development and validation of a prediction model for shared decision making for patients with lumbar spinal stenosis.North American Spine Society journal · 2024Article
- Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery.JAMA network open · 2024Article
- Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study.Frontiers in public health · 2024Article
- Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review.Brain sciences · 2023Review
- Predictive value of texture analysis on lumbar MRI in patients with chronic low back pain.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2023Observational
- Potential Applications of Artificial Intelligence and Machine Learning in Spine Surgery Across the Continuum of Care.International journal of spine surgery · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective/prospective study.
objectiveModels based on preoperative factors can predict patients' outcome at 1-year follow-up. This study measures the performance of several machine learning (ML) models and compares the results with conventional methods.
methodsInclusion criteria were patients who had lumbar disc herniation (LDH) surgery, identified in the Danish national registry for spine surgery. Initial training of models included 16 independent variables, including demographics and presurgical patient-reported measures. Patients were grouped by reaching minimal clinically important difference or not for EuroQol, Oswestry Disability Index, Visual Analog Scale (VAS) Leg, and VAS Back and by their ability to return to work at 1 year follow-up. Data were randomly split into training, validation, and test sets by 50%/35%/15%. Deep learning, decision trees, random forest, boosted trees, and support vector machines model were trained, and for comparison, multivariate adaptive regression splines (MARS) and logistic regression models were used. Model fit was evaluated by inspecting area under the curve curves and performance during validation.
resultsSeven models were arrived at. Classification errors were within ±1% to 4% SD across validation folds. ML did not yield superior performance compared with conventional models. MARS and deep learning performed consistently well. Discrepancy was greatest among VAS Leg models.
conclusionsFive predictive ML and 2 conventional models were developed, predicting improvement for LDH patients at the 1-year follow-up. We demonstrate that it is possible to build an ensemble of models with little effort as a starting point for further model optimization and selection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.