Detection of Cases

Table 23.6 summarizes the results of studies that are informative about Hypothesis 1: A chief complaint can discriminate between whether a patient has syndrome or disease X or not.

Methodologically, the studies measure the sensitivity and specificity with which different NLP methods (which we refer to as "classifiers") identify patients with a variety of syndromes using only the recorded chief complaints. The reference syndrome ("gold standard'') for patients in these studies was developed by physician review of narrative medical records, such as ED reports, or automatically from ICD-9 primary discharge diagnoses. Most studies have evaluated detection of syndromes in adults, whereas a single study examined detection of syndromes in pediatric patients (Beitel et al., 2004).

Table 23.6 groups the experiments by syndrome because many experiments studied the same or similar syndromes. Each row in Table 23.6 reports the sensitivity and specificity of a classifier for a particular syndrome, and the likelihood ratio

Note that time latencies associated with automatic batch transfer cannot necessarily be decreased by decreasing the periodicity of the batch transfer. The query to the healthcare information system that generates the batch file may be resource intensive and a healthcare system may only be willing to schedule the query during non peak load periods (e.g., midnight).

table 23.6 Performance of Bayesian and other classifiers in detecting syndromes

Reference Standard Classifier Being Tested for Comparison

Sensitivity

Specificity

Positive Likelihood Ratio (95% CI)

Negative Likelihood Ratio (95% CI)

Respiratory Syndrome Chief Complaint Bayesian Classifier Respiratory (CCBC)a CCBC4

CCBC a

Utah Department of Health (UDOH) Respiratory with fever

Human review of ED reports Utah ICD-9 list

CCBC a

Human review of ED reports Utah ICD-9 list

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