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ردیف | عنوان | نوع |
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1 |
Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests
گروه های یادگیری ماشینی بیماران براساس احتمال بهبود عملکرد زودهنگام بر اساس سنسورهای پوشیدنی ابزار تست شده به موقع قبل و بعد از عمل-2019 Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for
instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the
abundant information these systems provide and segment patients into relevant groups without specifying
group membership criteria. The objective of this study is to examine functional parameters
influencing favorable recovery outcomes by separating patients into functional groups and tracking them
through clinical follow-ups.
Methods: Patients undergoing primary unilateral total knee arthroplasty (n ¼ 68) completed instrumented
timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments.
A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients
into functionally distinguished groups based on the derived features. These groups were analyzed to
determine which metrics differentiated most and how each cluster improved during early recovery.
Results: Patients separated into 2 clusters (n ¼ 46 and n ¼ 22) with significantly different test completion
times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups
revealed 64% of one group improved their function while 63% of the other maintained preoperative
function. The higher improvement group shortened their test times by 4.94 s, (P ¼ .005) showing faster
recovery while the other group did not improve above a minimally important clinical difference (0.87 s,
P ¼.07). Features with the largest effect size between groups were distinguished as important functional
parameters.
Conclusion: This work supports using wearable sensors to instrument functional tests during clinical
visits and using machine learning to parse complex patterns to reveal clinically relevant parameters. Keywords: total knee arthroplasty | wearable sensors | machine learning | functional testing | early recovery |
مقاله انگلیسی |
2 |
Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
معیار ارزش پیش بینی یادگیری عمیق قبل از عمل برای اولیه آرتروپلاستی کامل زانو: توسعه و اعتبار مدل شبکه عصبی مصنوعی-2019 Background: The objective is to develop and validate an artificial neural network (ANN) that learns and
predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee
arthroplasty (TKA). The secondary objective applied the ANN to propose a risk-based, patient-specific
payment model (PSPM) commensurate with case complexity.
Methods: Using data from 175,042 primary TKAs from the National Inpatient Sample and an institutional
database, an ANN was developed to predict LOS, charges, and disposition using 15 preoperative variables.
Outcome metrics included accuracy and area under the curve for a receiver operating characteristic
curve. Model uncertainty was stratified by All Patient Refined comorbidity indices in establishing a riskbased
PSPM.
Results: The dynamic model demonstrated “learning” in the first 30 training rounds with areas under the
curve of 74.8%, 82.8%, and 76.1% for LOS, charges, and discharge disposition, respectively. The PSPM
demonstrated that as patient comorbidity increased, risk increased by 2.0%, 21.8%, and 82.6% for moderate,
major, and severe comorbidities, respectively.
Conclusion: Our deep learning model demonstrated “learning” with acceptable validity, reliability, and
responsiveness in predicting value metrics, offering the ability to preoperatively plan for TKA episodes of
care. This model may be applied to a PSPM proposing tiered reimbursements reflecting case complexity. Keywords: machine learning | total knee arthroplasty (TKA) | artificial neural network | deep learning | artificial intelligence |
مقاله انگلیسی |
3 |
Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?
پیش بینی پرداخت های بستری قبل از آرتروپلاستی با اندام تحتانی با استفاده از آموزش عمیق: کدام مدل معماری بهترین است؟-2019 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 |
مقاله انگلیسی |
4 |
Temporal Relations of Unplanned Readmissions Following Total Knee Arthroplasty: A Study of Large State Inpatient Databases
روابط زمانی برنامه ریزی نشده بستری مجدد در آرتروپلاستی کامل زانو: مطالعه پایگاه داده های سرپایی بزرگ دولتی-2017 Background: Centers for Medicare & Medicaid Services stipulate a 90-day global period for hospitals for
unplanned readmissions after primary total knee arthroplasty (TKA). However, not all readmissions are
directly attributable to index surgery, and reasons for readmissions vary during this time period. This
study identifies causes and temporal relations of readmissions using large state inpatient databases.
Methods: State inpatient databases of New York and California were queried for all primary TKAs
performed from 2005 to 2011 and frequencies of all causes of unplanned readmission were identified
from 0 to 90 days after index surgery using the International Classification of Diseases, Ninth Revision,
codes. Temporal differences in proportions of readmission diagnoses were tested using the Pearson chisquare test.
Results: The query identified 419,805 cases of primary TKA from 2005 to 2011. There were 26,924
readmissions during the 90-day recovery period, with 15,547 (57.7%) at 0-30 days, 6593 (24.5%) at 31-60
days, and 4784 (17.8%) at 61-90 days. Primary diagnoses at readmission that were identified to be directly
attributable to surgery comprised 38.3% readmissions at 0-30 days, 24.0% at 31-60 days, and 16.3% at
60-90 days. Proportion of readmissions directly attributable to surgery decreased over the 90-day period
after index surgery.
Conclusion: From this analysis of 2 large state inpatient databases, primary diagnoses at readmission vary
with time, and majority of these may not be directly attributable to index surgery or postoperative state
up to 90 days. These findings suggest that the current 90-day global period policy for this procedure
should be reformed to better reflect the profile of unplanned readmissions after TKA.
Keywords: HCUP | postoperative complication | readmission |state inpatient database | total knee arthroplasty |
مقاله انگلیسی |