عنوان انگلیسی مقاله:
Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?
ترجمه فارسی عنوان مقاله:
پیش بینی پرداخت های بستری قبل از آرتروپلاستی با اندام تحتانی با استفاده از آموزش عمیق: کدام مدل معماری بهترین است؟
Sciencedirect - Elsevier - The Journal of Arthroplasty, 34 (2019) 2235-2242: doi:10:1016/j:arth:2019:05:048
Jaret M. Karnuta, MS a, Sergio M. Navarro, MBA b, Heather S. Haeberle, BS c, J. Matthew Helm, BS d, Atul F. Kamath, MD a, Jonathan L. Schaffer, MD, MBA a, Viktor E. Krebs, MD a, Prem N. Ramkumar, MD, MBA
Background: Recent advances in machine learning have given rise to deep learning, which uses hierarchical
layers to build models, offering the ability to advance value-based healthcare by better predicting patient
outcomes and costs of a given treatment. The purpose of this study is to compare the performance of 2
common deep learning models, traditional multilayer perceptron (MLP), and the newer dense neural
network (DenseNet), in predicting outcomes for primary total hip arthroplasty (THA) and total knee
arthroplasty (TKA) as a foundation for future musculoskeletal studies seeking to utilize machine learning.
Methods: Using 295,605 patients undergoing primary THA and TKA from a New York State inpatient
administrative database from 2009 to 2016, 2 neural network designs (MLP vs DenseNet) with different
model regularization techniques (dropout, batch normalization, and DeCovLoss) were applied to
compare model performance on predicting inpatient procedural cost using the area under the receiver
operating characteristic curve (AUC). Models were implemented to identify high-cost surgical cases.
Results: DenseNet performed similarly to or better than MLP across the different regularization techniques
in predicting procedural costs of THA and TKA. Applying regularization to DenseNet resulted in a
significantly higher AUC as compared to DenseNet alone (0.813 vs 0.792, P ¼ .011). When regularization
methods were applied to MLP, the AUC was significantly lower than without regularization (0.621 vs
0.791, P ¼ 1.1 1015). When the optimal MLP and DenseNet models were compared in a head-to-head
fashion, they performed similarly at cost prediction (P > .999).
Conclusion: This study establishes that in predicting costs of lower extremity arthroplasty, DenseNet
models improve in performance with regularization, whereas simple neural network models perform
significantly worse without regularization. In light of the resource-intensive nature of creating and
testing deep learning models for orthopedic surgery, particularly for value-centric procedures such as
arthroplasty, this study establishes a set of key technical features that resulted in better prediction of
inpatient surgical costs. We demonstrated that regularization is critically important for neural networks
in arthroplasty cost prediction and that future studies should utilize these deep learning techniques to
predict arthroplasty costs.
Level of Evidence: III.
Keywords: machine learning | deep learning | neural networks | big data | total knee arthroplasty | total hip arthroplasty