ArticleAmerican journal of surgery2021
Ensemble machine learning for the prediction of patient-level outcomes following thyroidectomy.
Article in American journal of surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predicting Hungry Bone Syndrome with Interpretable Machine Learning: A Single-Center Cohort of Dialysis Patients Undergoing Parathyroidectomy.Diagnostics (Basel, Switzerland) · 2026Article
- Quantitative analysis of studies that use artificial intelligence on thyroid cancer: a 20-year bibliometric analysis.Frontiers in oncology · 2025Review
- Predictors of postoperative complications following thyroidectomy: A systematic review.Surgery in practice and science · 2024Review
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome.Healthcare informatics research · 2023Article
- The future of artificial intelligence in neurosurgery: A narrative review.Surgical neurology international · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundAccurate prediction of thyroidectomy complications is necessary to inform treatment decisions. Ensemble machine learning provides one approach to improve prediction.
methodsWe applied the Super Learner (SL) algorithm to the 2016-2018 thyroidectomy-specific NSQIP database to predict complications following thyroidectomy. Cross-validation was used to assess model discrimination and precision.
resultsFor the 17,987 patients undergoing thyroidectomy, rates of recurrent laryngeal nerve injury, post-operative hypocalcemia prior to discharge or within 30 days, and neck hematoma were 6.1%, 6.4%, 9.0%, and 1.8%, respectively. SL improved prediction of thyroidectomy-specific outcomes when compared with benchmark logistic regression approaches. For postoperative hypocalcemia prior to discharge, SL improved the cross-validated AUROC to 0.72 (95%CI 0.70-0.74) compared to 0.70 (95%CI 0.68-0.72; p < 0.001) when using a manually curated logistic regression algorithm.
conclusionEnsemble machine learning modestly improves prediction for thyroidectomy-specific outcomes. SL holds promise to provide more accurate patient-level risk prediction to inform treatment decisions.
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