ArticleNeurospine2019
Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.
Article in Neurospine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.BMC medical informatics and decision making · 2025Article
- Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation.Scientific reports · 2025Article
- A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning.Frontiers in medicine · 2025Article
- Observational
- Prediction of adolescent idiopathic scoliosis with machine learning algorithms using brain volumetric measurements.JOR spine · 2024Article
- Commentary on "Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery".Neurospine · 2024Article
- Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement.PloS one · 2024Article
- Predicting female pelvic tilt and lumbar angle using machine learning in case of urinary incontinence and sexual dysfunction.Scientific reports · 2023Article
- Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.Scientific reports · 2023Article
- Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration.Clinical and experimental emergency medicine · 2022Article
- Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?Global spine journal · 2022Article
- Spine Surgery Assisted by Augmented Reality: Where Have We Been?Yonsei medical journal · 2022Review
- A multi-step approach to managing missing data in time and patient variant electronic health records.BMC research notes · 2022Article
- Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery.Acta neurochirurgica. Supplement · 2022Article
- Emerging Technologies in the Treatment of Adult Spinal Deformity.Neurospine · 2021Article
- Preoperative Radiological Parameters to Predict Clinical and Radiological Outcomes after Laminoplasty.Journal of Korean Neurosurgical Society · 2021Article
- Machine learning-based approach for disease severity classification of carpal tunnel syndrome.Scientific reports · 2021Article
- Application of the Preoperative Assistant System Based on Machine Learning in Hepatocellular Carcinoma Resection.Journal of healthcare engineering · 2021Article
- A narrative review of machine learning as promising revolution in clinical practice of scoliosis.Annals of translational medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.
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What OpenQuestion holds
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.