tetano
Editor, Senior Moderator
J Public Health (Oxf). 2017 Oct 20:1-8. doi: 10.1093/pubmed/fdx141. [Epub ahead of print]
[h=1]Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method.[/h] Tsui F[SUP]1,[/SUP][SUP]2[/SUP], Ye Y[SUP]1,[/SUP][SUP]2[/SUP], Ruiz V[SUP]1[/SUP], Cooper GF[SUP]1,[/SUP][SUP]2[/SUP], Wagner MM[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Objectives:[/h] To assess the performance of a Bayesian case detector (BCD) for influenza surveillance and clinical diagnosis.
[h=4]Methods:[/h] BCD uses a Bayesian network classifier to compute the posterior probability of a patient having influenza based on 31 findings from narrative clinical notes. To assess the potential for disease surveillance, we calculated area under the receiver operating characteristic curve (AUC) to indicate BCD's ability to differentiate between influenza and non-influenza encounters in emergency department settings. To assess the potential for clinical diagnosis, we measured AUC for diagnosing influenza cases among encounters having influenza-like illnesses. We also evaluated the performance of BCD using dynamically estimated influenza prevalence, and measured sensitivity, specificity and positive predictive value.
[h=4]Results:[/h] For influenza surveillance, BCD differentiated between influenza and non-influenza encounters well with an AUC of 0.90 and 0.97 with dynamic influenza prevalence (P < 0.0001). For clinical diagnosis, the addition of dynamic influenza prevalence to BCD significantly improved AUC from 0.63 to 0.85 to distinguish influenza from other causes of influenza-like illness.
[h=4]Conclusions and policy implications:[/h] BCD can serve as an influenza surveillance and a differential diagnosis tool via our dynamic prevalence approach. It enhances the communication between public health and clinical practice.
[h=4]KEYWORDS:[/h] Bayesian case detector; dynamic prior; emergency department notes; influenza; influenza-like illness
PMID: 29059331 DOI: 10.1093/pubmed/fdx141
[h=1]Automated influenza case detection for public health surveillance and clinical diagnosis using dynamic influenza prevalence method.[/h] Tsui F[SUP]1,[/SUP][SUP]2[/SUP], Ye Y[SUP]1,[/SUP][SUP]2[/SUP], Ruiz V[SUP]1[/SUP], Cooper GF[SUP]1,[/SUP][SUP]2[/SUP], Wagner MM[SUP]1,[/SUP][SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]Objectives:[/h] To assess the performance of a Bayesian case detector (BCD) for influenza surveillance and clinical diagnosis.
[h=4]Methods:[/h] BCD uses a Bayesian network classifier to compute the posterior probability of a patient having influenza based on 31 findings from narrative clinical notes. To assess the potential for disease surveillance, we calculated area under the receiver operating characteristic curve (AUC) to indicate BCD's ability to differentiate between influenza and non-influenza encounters in emergency department settings. To assess the potential for clinical diagnosis, we measured AUC for diagnosing influenza cases among encounters having influenza-like illnesses. We also evaluated the performance of BCD using dynamically estimated influenza prevalence, and measured sensitivity, specificity and positive predictive value.
[h=4]Results:[/h] For influenza surveillance, BCD differentiated between influenza and non-influenza encounters well with an AUC of 0.90 and 0.97 with dynamic influenza prevalence (P < 0.0001). For clinical diagnosis, the addition of dynamic influenza prevalence to BCD significantly improved AUC from 0.63 to 0.85 to distinguish influenza from other causes of influenza-like illness.
[h=4]Conclusions and policy implications:[/h] BCD can serve as an influenza surveillance and a differential diagnosis tool via our dynamic prevalence approach. It enhances the communication between public health and clinical practice.
[h=4]KEYWORDS:[/h] Bayesian case detector; dynamic prior; emergency department notes; influenza; influenza-like illness
PMID: 29059331 DOI: 10.1093/pubmed/fdx141