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Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models

To fairly compare our method with ICD-based PI identification algorithms, the classification threshold was tuned to achieve similarly estimated specificities (0.988 and 0.959) of 2 ICD-based algorithms developed and validated in a previous study by Ho et al [1]. The first case definition is more specific and yields greater detection precision. The second definition is more inclusive of nonspecific codes for wounds and is likely to capture a larger number of cases.

Elvira Nurmambetova, Jie Pan, Zilong Zhang, Guosong Wu, Seungwon Lee, Danielle A Southern, Elliot A Martin, Chester Ho, Yuan Xu, Cathy A Eastwood

JMIR AI 2023;2:e41264