Abstract: A patient is monitored for a medical condition such as acute lung injury (AL1) by operations including: (i) receiving values of a plurality of physiological parameters for the patient; (ii) computing an AL1 indicator value based at least on the received values of the plurality of physiological parameters for the patient; and (iii) displaying a representation of the computed AL1 indicator value on a display (14, 22). The computing operation (ii) may employ various inference algorithms trained on a training set comprising reference patients to distinguish between reference patients having AL1 and reference patients not having AL1, or may employ an aggregation of two or more such inference algorithms. If patients in an ICU are monitored, the display (22) may simultaneously display a diagrammatic representation of each patient including an identification of the patient and a representation of the AL1 indicator value for the patient.

Abstract: When generating a model of physiological systems in a patient, differential equations representing parameters and variables in the systems are linked together to form one or more sub-models (e.g., one for each physiological system), which in turn are linked together to form the patient model. Simulations of hypothetical clinical situations are then run on the model to solve for the variables, and the solutions are output as decision support data for review by a clinician to facilitate a determination of a treatment of diagnosis for the patient. Additionally, model predictions can be compared to actual measurements, when available, and the model can be refined or optimized as a function of the comparison.

Abstract: When generating a model of physiological systems in a patient, differential equations representing parameters and variables in the systems are linked together to form one or more sub-models (e.g., one for each physiological system), which in turn are linked together to form the patient model. Simulations of hypothetical clinical situations are then run on the model to solve for the variables, and the solutions are output as decision support data for review by a clinician to facilitate a determination of a treatment of diagnosis for the patient. Additionally, model predictions can be compared to actual measurements, when available, and the model can be refined or optimized as a function of the comparison.