Figure 2. Results of genetic algorithm search.

(A) shows the frequency of each “gene” (clinical variable) presented in stored “chromosomes” (a combination of clinical variables). The top 50 variables were colored, and the top seven variables were named. (B) displays the stability of the rank of the top 50 variables. It appeared that the top four variables stabilized quicker. (C) shows the distribution of the number of generations required for an evolution to achieve the fitness goal. If an evolution epoch cannot reach the fitness goal of AUC = 0.77, the iteration is considered as “no solution” and the current iteration stopped. The training sample was split into the training and test sets in 2:1 ratio. Annotations: aids: acquired immunodeficiency syndrome; tumor: metastatic tumor; leuk: leukemia; hepa: hepatic failure; bilih: highest bilirubin; fio2: Fraction of Inspired Oxygen; albuml: lowest albumin; immune: immunodeficiency; hcth: highest value of hematocrit; reside: residence prior to admission; admitfrom: admission source; gluch: highest glucose; pip: peak inspiratory pressure on day 0; resp: respiratory rate on day 0; sodiumh: highest sodium value.