DNF literals explanation.

<p>Note.</p><p>*: when missing values present in the data, they are treated as a literal, but they are never selected in the DNF learning.</p

The supporting information contains additional results.

(PDF)</p

Availability of data across physiologic domains.

<p>Of 1815 patients with cytokine data on day 1, much smaller numbers of patients had single nucleotide profiles (SNP), Fluorescent-Antibody Cell Sorting (FACS) measurements of surface markers, or full coagulation studies (Coags)performed.</p

Prediction performance of DNF learning on hospital and 90-day mortality data.

<p>10-fold cross validation is applied to assess the prediction performance of DNF learning on hospital and 90-day mortality, and compare the performance when using the whole feature set (Model 8, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089053#pone-0089053-t001" target="_blank">Table 1</a>) and only day 1 (Model 7) and/or day 2 cytokine (Model 7 + day 2 cytokines).</p

DNF of the patient mortality.

<p>DNF of the patient mortality.</p

Comparative performance of models on predicting 90-day mortality.

<p>NB-Naive Bayes, SVM-Support vector machine, NN-neural network, LOG-Logistic regression, BL-Boosted logistic regression, RT-Random tree, RF-Random forest, DNF-Disjunctive Normal Form learning.</p

Interpreting DNF models on three patients.

<p>The prediction procedure of DNF is represented in three layers: the top layer is the DNF itself; the middle layer is the clause level; and the bottom layer is the final outcome. Red color rectangles indicate that patient data is above the threshold and a severity condition is met; green rectangles indicate that patient data is below and the condition is not met. Three example patients are shown. For patient A, , and are all above the threshold and results in a positive Clause 2 so the predicted outcome is mortality. For patient B, Clause 2 is negative due to the low (procalcitonin in the lowest quartile); however high turns on Clause 1 and predicts mortality too. Patient C has high but it is not sufficient to turn on either Clause 1 or 2 and she is therefore predicted to survive.</p

Predictors (features) inluded in the different models.

<p>Predictors (features) inluded in the different models.</p

Characteristic epidemiological and evolutionary dynamics are observed within different parameter regimes.

<p>For each parameterization, a 10 year excerpt from a typical simulation is shown. Graphs on the left show weekly percent incidence (northern hemisphere only, green) and weekly mean pairwise antigenic diversity (blue). Radial dendrograms on the right show phylogeny (red) with concentric rings marking one year intervals. <b>(A)</b> Weak and short-lived strain-transcending immunity (strength = 25%, half-life≈1.3 months). Peak incidence and attack rate are greatly elevated; diversity is unconstrained; and phylogeny is exceedingly branched. <b>(B)</b> Strong and long-lived strain-transcending immunity (strength = 100%, half-life≈11 years). Sporadic outbreaks result in greatly reduced peak incidence and attack rate; antigenic diversity approaches zero during extended periods of near-extinction; and phylogeny is exceedingly slender. <b>(C)</b> Parameterization similar to that described in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125047#pone.0125047.ref004" target="_blank">4</a>]: strong and short-lived strain-transcending immunity (strength = 100%, half-life≈7 months). Regular annual epidemics with an attack rate of around 15% are observed; antigenic diversity takes on the familiar pattern of gradual build-up followed by rapid collapse coinciding with extinction of prominent lineages; and phylogeny is generally linear over long time spans, with a moderate amount of short-term branching. <b>(D)</b> Alternative parameterization within the plausible region: moderate strength and intermediate duration strain-transcending immunity (strength≈63%, half-life≈28 months). As in part C, all measures are generally within acceptable ranges, and flu-like dynamics are reliably generated.</p

Epidemiological parsimony is measured across transient strain-transcending immunity parameter space.

<p>Sixty parameterizations of strain-transcending immunity (strength and duration) were simulated. Within each map, the 60 parameterizations are represented by a set of circles and semicircles; the inner circle at each point represents the sample mean of the measure, and the top and bottom semi-circles represent the mean plus and minus one sample standard deviation, respectively (n = 20 realizations for each point). Color corresponds to the agreement between the simulated outcome and the expected outcome for influenza; blue indicates a value below the 95% CI for influenza, red indicates a value above the 95% CI for influenza, and green represents the expected value for influenza. The 95% CI for influenza is marked on each color scale, and the target value is indicated by ‘⊕’. The tip of the “FGB’03” arrow indicates the default parameterization given in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125047#pone.0125047.ref004" target="_blank">4</a>]. Triangulation and interpolation were used to achieve smooth shading throughout the space to facilitate visual identification of spatial trends. Faint contour lines demarcate confidence interval boundaries. <b>(A)</b> AAR measured from model output; target value is 0.15 (95% CI: ±0.10). <b>(B)</b> Epidemic duration measured from model output; target value is 12 (95% CI: ±2) weeks. <b>(C)</b> R_p measured from model output; target value is 1.3 (95% CI: ±0.2). <b>(D)</b> Peak weekly incidence measured from model output; target value is 0.025 (95% CI: ±0.015).</p