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Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients
40
Zitationen
3
Autoren
2020
Jahr
Abstract
The NLP algorithm used in this study was able to identify a subset of variables from randomly selected unstructured notes in arthroplasty with an accuracy above 90%. For some variables, such as objective exam data, the accuracy was very high. Our findings suggest that automated algorithms using NLP can help orthopaedic practices retrospectively collect information for registries and quality improvement (QI) efforts. Cite this article: <i>Bone Joint J</i> 2020;102-B(7 Supple B):99-104.
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