The HD-SIM-MAP dataset is a synthetic (model-based) dataset generated to enable the study of mean arterial blood pressure (MAP) changes during hemodialysis (HD).The dataset includes the profiles of MAP changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted.  Ultrafiltration was set randomly within ±1 L from the assigned fluid overload.  All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).

The HD-SIM-MAP dataset is a synthetic (model-based) dataset generated to enable the study of mean arterial blood pressure (MAP) changes during hemodialysis (HD).The dataset includes the profiles of MAP changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted.  Ultrafiltration was set randomly within ±1 L from the assigned fluid overload.  All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).

The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted.  Ultrafiltration was set randomly within ±1 L from the assigned fluid overload.  All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below). When using the dataset, please cite the associated conference paper:Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.

The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted.  Ultrafiltration was set randomly within ±1 L from the assigned fluid overload.  All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below). When using the dataset, please cite the associated conference paper:Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.