A computer system for identifying a patient undergoing periodic hemodialysis treatments at increased risk of death, the computer system comprising: a) a user input means for determining patient data from a user; b) a digital processor coupled to receive determined patient data from the user input means, wherein the digital processor executes a modeling system in working memory, wherein the modeling system: i) selects one or more clinical or biochemical parameters selected from a statistically significant percent change in the patient's serum albumin level, a statistically significant percent change in the patient's pre-dialysis weight, and a statistically significant percent change in the patient's ferritin level, while the patient is undergoing periodic hemodialysis treatment, wherein a statistically significant percent change is defined by a P< 0.05; ii) estimates the probability of death of the patient over a future time interval by a logistic regression model including model coefficients, the model coefficients determined by analyzing data from patients that were undergoing periodic hemodialysis treatments, the analysis including a longitudinal analysis in time on the one or more percent change in the clinical or biochemical parameters of the patients; and iii) identifies the patient as having an increased risk of death if the probability of death of the patient is greater than a predetermined threshold probability; and c) an output means coupled to the digital processor, the output means provides to the user the probability of death of the patient over the future time interval while the patient is undergoing periodic hemodialysis treatments. a) a user input means for determining patient data from a user; b) a digital processor coupled to receive determined patient data from the user input means, wherein the digital processor executes a modeling system in working memory, wherein the modeling system: i) selects one or more clinical or biochemical parameters selected from a statistically significant percent change in the patient's serum albumin level, a statistically significant percent change in the patient's pre-dialysis weight, and a statistically significant percent change in the patient's ferritin level, while the patient is undergoing periodic hemodialysis treatment, wherein a statistically significant percent change is defined by a P< 0.05; ii) estimates the probability of death of the patient over a future time interval by a logistic regression model including model coefficients, the model coefficients determined by analyzing data from patients that were undergoing periodic hemodialysis treatments, the analysis including a longitudinal analysis in time on the one or more percent change in the clinical or biochemical parameters of the patients; and iii) identifies the patient as having an increased risk of death if the probability of death of the patient is greater than a predetermined threshold probability; and i) selects one or more clinical or biochemical parameters selected from a statistically significant percent change in the patient's serum albumin level, a statistically significant percent change in the patient's pre-dialysis weight, and a statistically significant percent change in the patient's ferritin level, while the patient is undergoing periodic hemodialysis treatment, wherein a statistically significant percent change is defined by a P< 0.05; ii) estimates the probability of death of the patient over a future time interval by a logistic regression model including model coefficients, the model coefficients determined by analyzing data from patients that were undergoing periodic hemodialysis treatments, the analysis including a longitudinal analysis in time on the one or more percent change in the clinical or biochemical parameters of the patients; and iii) identifies the patient as having an increased risk of death if the probability of death of the patient is greater than a predetermined threshold probability; and c) an output means coupled to the digital processor, the output means provides to the user the probability of death of the patient over the future time interval while the patient is undergoing periodic hemodialysis treatments.