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Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty

https://doi.org/10.1016/j.arth.2020.05.061
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Patient Satisfaction after Total Knee Arthroplasty: Who is Satisfied and Who is Not?
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Patient Reported Allergies Are a Risk Factor for Poor Outcomes in Total Hip and Knee Arthroplasty
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Patient Dissatisfaction Following Total Knee Arthroplasty: A Systematic Review of the Literature
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Preoperative Patient Profile in Total Hip and Knee Arthroplasty: Predictive of Increased Medicare Payments in a Bundled Payment Model
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Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model
inferredcites →https://doi.org/10.1016/j.arth.2018.12.020openalex-snapshot
Internal Validation of a Predictive Model for Satisfaction After Primary Total Knee Arthroplasty
inferredcites →https://doi.org/10.1016/j.arth.2019.05.034openalex-snapshot
Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model
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Development of Machine Learning Algorithms to Predict Clinically Meaningful Improvement for the Patient-Reported Health State After Total Hip Arthroplasty
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Patient Satisfaction after Total Knee Arthroplasty

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Logistic Regression and Machine Learning Models Cannot Discriminate Between Satisfied and Dissatisfied Total Knee Arthroplasty Patients
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