Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing.

ABSTRACT:

Background

Periprosthetic joint infection (PJI) data elements are contained in both structured and unstructured documents in electronic health records and require manual data collection. The goal of this study is to develop a natural language processing (NLP) algorithm to replicate manual chart review for PJI data elements.

Methods

PJI was identified among all total joint arthroplasty (TJA) procedures performed at a single academic institution between 2000 and 2017. Data elements that comprise the Musculoskeletal Infection Society (MSIS) criteria were manually extracted and used as the gold standard for validation. A training sample of 1208 TJA surgeries (170 PJI cases) was randomly selected to develop the prototype NLP algorithms and an additional 1179 surgeries (150 PJI ca

SUBMITTER: Fu S 

PROVIDER: S-EPMC7855617 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature