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Title:

Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data.

Document type:
Journal Article
Author(s):
Hinterwimmer, Florian; Lazic, Igor; Langer, Severin; Suren, Christian; Charitou, Fiona; Hirschmann, Michael T; Matziolis, Georg; Seidl, Fritz; Pohlig, Florian; Rueckert, Daniel; Burgkart, Rainer; von Eisenhart-Rothe, Rüdiger
Abstract:
PURPOSE: The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed....     »
Journal title abbreviation:
Knee Surg Sports Traumatol Arthrosc
Year:
2023
Journal volume:
31
Journal issue:
4
Pages contribution:
1323-1333
Fulltext / DOI:
doi:10.1007/s00167-022-06957-w
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35394135
Print-ISSN:
0942-2056
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Klinik und Poliklinik für Orthopädie und Sportorthopädie (Prof. von Eisenhart-Rothe); Klinik und Poliklinik für Unfallchirurgie (Prof. Biberthaler)
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