IndraLab

Statements

CALM1 binds NINL. 19 / 19
| 19
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
"The agreements between ICD, flowsheet, and CAM-NLP are provided in xref ."
35672994
sparser
"Pagali et al. xref found that their NLP-CAM algorithm achieved 80% sensitivity compared to 55% for ICD codes and 43% for nursing flowsheets."
41266514
sparser
"We applied NLP-CAM to three test sites (UTH, University of Alabama at Birmingham (UAB), and Vanderbilt University Medical Center (VUMC)) and reported the out-of-the-box performance, as shown in Table  xref ."
40979101
sparser
"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
"NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%."
36283056
sparser
"NLP-CAM is an NLP-powered computational phenotyping tool that can identify a patient’s delirium status from the EHR [ xref ]."
40979101
sparser
"Agreement between ICD and CAM-NLP was moderate-high (k = 0.61)."
35672994
sparser
"Similarly, agreement between CAM-NLP and flowsheet was moderate (k = 0.42)."
35672994
sparser
"Although CAM-NLP yielded the highest agreement, no single data type comprehensively represented the delirium status."
35672994
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
sparser
"Fu et al. xref developed the NLP-CAM algorithm using the MedTaggerIE framework, mapping delirium-related keywords to CAM features with assertion detection for negation handling."
41266514
sparser
"NLP-CAM demonstrated a sensitivity, specificity and accuracy of 0.919, 1.000 and 0.967, respectively."
33125037
sparser
"Another retrospective study on the efficacy of an AI-based algorithm, the Natural Language Processing Confusion Assessment Method (NLP-CAM), in detecting delirium based on EMRs of COVID-19 patients admitted to hospitals revealed an increased rate of detection by the NLP-CAM algorithm of 80% as compared to physician diagnosis of 55% [ xref ]."
38846182
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
"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 demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively."
35239951