当前位置： SCI文献检索 > JOURNAL OF ORTHOPAEDIC TRAUMA期刊下所有文献 > A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide "Malleolus First" Fixation.

A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide "Malleolus First" Fixation.

Abstract：

OBJECTIVES:To develop an accurate machine learning (ML) predictive model incorporating patient, fracture, and trauma characteristics to identify individual patients at risk of an (occult) PMF. METHODS:Databases of 2 studies including patients with TSFs from 2 Level 1 trauma centers were combined for analysis. Using ten-fold cross-validation, 4 supervised ML algorithms were trained in recognizing patterns associated with PMFs: (1) Bayes point machine; (2) support vector machine; (3) neural network; and (4) boosted decision tree. Performance of each ML algorithm was evaluated and compared based on (1) C-statistic; (2) calibration slope and intercept; and (3) Brier score. The best-performing ML algorithm was incorporated into an online open-access prediction tool. RESULTS:Total data set included 263 patients, of which 28% had a PMF. Training of the Bayes point machine resulted in the best-performing prediction model reflected by good C-statistic, calibration slope, calibration intercept, and Brier score of 0.89, 1.02, -0.06, and 0.106, respectively. This prediction model was deployed as an open-access online prediction tool. CONCLUSION:A ML-based prediction model accurately predicted the probability of a (occult) PMF in patients with a TSF based on patient- and fracture-specific characteristics. This prediction model can guide surgeons in their diagnostic workup and preoperative planning. Further research is required to externally validate the model before implementation in clinical practice. LEVEL OF EVIDENCE:Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

journal_name

J Orthop Trauma

journal_title

Journal of orthopaedic trauma

authors

Hendrickx LAM,Sobol GL,Langerhuizen DWG,Bulstra AEJ,Hreha J,Sprague S,Sirkin MS,Ring D,Kerkhoffs GMMJ,Jaarsma RL,Doornberg JN,Machine Learning Consortium.

doi

10.1097/BOT.0000000000001663

subject

Has Abstract

pub_date

2020-03-01 00:00:00

pages

131-138

issue

eissn

0890-5339

issn

1531-2291

pii

00005131-202003000-00004

journal_volume

pub_type

杂志文章

在线工具

A Machine Learning Algorithm to Predict the Probability of (Occult) Posterior Malleolar Fractures Associated With Tibial Shaft Fractures to Guide "Malleolus First" Fixation.