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

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

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

DNF of the patient mortality.

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

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

Predictors (features) inluded in the different models.

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

Summary of bacteria-strain-dependent parameters in each of the three studies presented.

<p>Summary of bacteria-strain-dependent parameters in each of the three studies presented.</p

Analysis of bacteria-dependent parameters in the neuraminidase study.

<p>(A) One-dimensional parameter distributions of bacteria-dependent parameters: <i>q</i>, <i>a</i>, <i>ν</i>, <i>ξ</i>_<i>nl</i>, <i>ξ</i>_<i>nb</i>. The top row (green) shows ensembles for NanA⁻ bacteria. The middle row (red) shows ensembles for NanB⁻ bacteria. The bottom row (blue) shows ensembles for wild-type (WT) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Two-dimensional parameter correlations for NanA⁻ (C) NanB⁻ (D) and wild-type (E) bacteria.</p

Ensemble trajectories of serotype study.

<p>Ensemble fits of each strain for lung pathogen (<i>P</i>_<i>L</i>), blood pathogen (<i>P</i>_<i>B</i>), epithelial damage (<i>D</i>), and activated phagocytic cells (<i>N</i>). The black line represents the median trajectory, the inner dark gray area represents the 25^th to 75^th quantiles of trajectories, and the outer light gray envelope represents 90% of the trajectories (5^th to 95^th quantiles). Data points with standard deviations are represented by the black triangles with error bars. Data were taken at 12, 24, and 48 hours post-infection with three mice in each group. Trajectories are simulated over two days, with infection occurring on day 0 in the blood. The top row shows ensembles for 0100993 bacteria, the middle row shows ensembles for TIGR4 bacteria, and the bottom row shows ensembles for the D39 bacteria.</p