IndraLab

Statements

CALM1 binds NINL. 10 / 10
| 10
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"This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection."
36283056
sparser
"A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm."
36283056
sparser
"Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics."
36283056
sparser
"NLP-CAM demonstrated a sensitivity, specificity and accuracy of 0.919, 1.000 and 0.967, respectively."
33125037
sparser
"The prevalence of delirium cases was examined using International Classification of Diseases, 9th Revision (ICD-9), NLP-CAM, and NLP-mCAM."
35239951
sparser
"NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%."
36283056
sparser
"Two NLP algorithms were developed based on CAM criteria: one based on the original CAM (NLP-CAM; delirium vs no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium)."
35239951
sparser
"NLP-CAM demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively."
35239951
sparser
"Two NLP algorithms were developed based on CAM criteria; one based on the original CAM (NLP-CAM; delirium vs. no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium)."
33125037
sparser
"The prevalence of delirium cases was examined using ICD-9, NLP-CAM, and NLP-mCAM."
33125037