Shapiro, LindaJablonowski, Karl2019-08-142019-08-142019Jablonowski_washington_0250E_20086.pdfhttp://hdl.handle.net/1773/43932Thesis (Ph.D.)--University of Washington, 2019An intelligent agent framework is used on an ICU EMR to create prediction models for disease onset. Eleven models are created to inspect 5 diseases: acute respiratory distress syndrome (ARDS); severe acute hypoxemic respiratory failure (SAHRF); acute kidney injury (AKI); sepsis; and disseminated intravascular coagulation (DIC). Four of the models (ARDS, AKI Stage 1, AKI Stage 2, and sepsis) are competitive or superior to the best comparable peer-reviewed models. The other seven are novel, including: SAHRF (AUC=0.952); DIC from ARDS positive patients (AUC=0.722); ARDS from DIC positive patients (AUC=0.675); AKI Stage 3 (AUC=0.983); the progression from AKI Stage 1 to Stage 2 (AUC=0.930); the progression from AKI Stage 2 to Stage 3 (AUC=0.951); and DIC (AUC=0.838). In derivative work: a correlation between pre-DIC patients and metabolic acidosis is shown, a meta-analysis on misclassified patients is given, a disease pathway that demon- strates how ARDS and DIC can interact in a positive feedback loop is presented. DIC is shown to be implicated in 78% of all in-hospital mortality of ARDS patients.application/pdfen-USnoneAKIARDSdata miningDICintelligent agentsepsisBioinformaticsMedicineComputer scienceBiomedical and health informaticsThesis