1. Figure 1 Bayesian network for influenza detection (GeNIe24 visualization).
From: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers
J Am Med Inform Assoc. 2014;21(5): doi: /amiajnl
J Am Med Inform Assoc | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

2. Figure 2 Percentages of influenza cases and shigellosis cases with targeted influenza-related findings.
From: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers
J Am Med Inform Assoc. 2014;21(5): doi: /amiajnl
J Am Med Inform Assoc | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

3. Figure 3 Log₁₀ LR⁺ (likelihood ratios) of features in expert-defined BN, BN-EM-Topaz, and BN-EM-MedLEE.
From: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers
J Am Med Inform Assoc. 2014;21(5): doi: /amiajnl
J Am Med Inform Assoc | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to

4. Figure 4 Log₁₀ LR⁻ (likelihood ratios) of features in expert-defined BN, BN-EM-Topaz, and BN-EM-MedLEE.
From: Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers
J Am Med Inform Assoc. 2014;21(5): doi: /amiajnl
J Am Med Inform Assoc | Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to