Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Journal: Journal of shoulder and elbow surgery

PMID: 31383411

Abstract

BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.

Authors

Anirudh K Gowd

Wake Forest University Baptist Medical Center, Winston-Salem, NC, USA. Electronic address: anirudhkgowd@gmail.com.
Avinesh Agarwalla

Westchester Medical Center, Valhalla, NY, USA.
Nirav H Amin

Veterans Affairs Loma Linda, Loma Linda, CA, USA.
Anthony A Romeo

Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
Gregory P Nicholson

Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
Nikhil N Verma

Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
Joseph N Liu

Loma Linda University Medical Center, Loma Linda, CA, USA.

Keywords

Age Factors Aged Arthroplasty, Replacement, Shoulder Blood Transfusion Body Mass Index Comorbidity Databases, Factual Female Forecasting Hematocrit Humans Length of Stay Male Middle Aged Operative Time Patient Readmission Postoperative Complications Predictive Value of Tests Reoperation ROC Curve Serum Albumin Smoking Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (31383411)

Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Abstract

Authors

Keywords

External Resources

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