Abstract: A computing system for determining a systematic training strategy for the user is provided. The computing system includes a user device that uses one or more sensors to obtain partial pressure of oxygen (PO2) levels of a user over a period of time. The user device provides previous PO2 levels to a personalized erythropoiesis model generation computing platform. The computing platform obtains individualized user data for the user indicating or more previous hematocrit and/or hemoglobin measurements for the user. The computing platform determines an individualized erythropoiesis model for the user based on the one or more previous hematocrit and/or hemoglobin measurements and the previous PO2 information, and employs the individualized erythropoiesis model to determine predicted hematocrit and/or hemoglobin measurements. The computing platform performs one or more actions based on the one or more predicted hematocrit and/or hemoglobin measurements.

Abstract: A computing system for determining a systematic training strategy for the user is provided. The computing system includes a user device that uses one or more sensors to obtain partial pressure of oxygen (PO2) levels of a user over a period of time. The user device provides previous PO2 levels to a personalized erythropoiesis model generation computing platform. The computing platform obtains individualized user data for the user indicating or more previous hematocrit and/or hemoglobin measurements for the user. The computing platform determines an individualized erythropoiesis model for the user based on the one or more previous hematocrit and/or hemoglobin measurements and the previous PO2 information, and employs the individualized erythropoiesis model to determine predicted hematocrit and/or hemoglobin measurements. The computing platform performs one or more actions based on the one or more predicted hematocrit and/or hemoglobin measurements.

Abstract: A method for determining a next hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) dosage for a first patient using a patient HIF-PHI model is provided. The method includes obtaining population patient data indicating HIF-PHI dosages and hemoglobin measurements for the patients. Then, virtual patient avatars are generated based on the population patient data. Each of the virtual patient avatars indicates a set of personalized model parameters for a HIF-PHI model. A plurality of HIF-PHI models are determined for the virtual patient avatars. Using the HIF-PHI models, one or more HIF-PHI treatment schemes for administering the HIF-PHI dosages is determined. Subsequently, the HIF-PHI treatment schemes along with a hematocrit and/or hemoglobin concentration for a patient are used to determine a next HIF-PHI dosage for the patient, and the next HIF-PHI dosage is administered for the patient.

Abstract: Described herein are computer systems and methods for generating, in silico, one or more virtual patients. The one or more virtual patients can be used, for example, to conduct a virtual clinical trial, such as to determine an efficacy of a therapy or medical device. The one or more virtual patients mathematically represent a physiological system in an actual patient for a health condition. Also, the one or more virtual patients can be used, for example, to determine an adverse effect from a therapy or any other deviation from a therapy, or compliance of a patient suffering from a health condition on a therapeutic protocol.

Abstract: Described herein are computer systems and methods for generating, in silico, one or more virtual patients. The one or more virtual patients can be used, for example, to conduct a virtual clinical trial, such as to determine an efficacy of a therapy or medical device. The one or more virtual patients mathematically represent a physiological system in an actual patient for a health condition. Also, the one or more virtual patients can be used, for example, to determine an adverse effect from a therapy or any other deviation from a therapy, or compliance of a patient suffering from a health condition on a therapeutic protocol.

Abstract: Described herein are computer systems and methods for generating, in silico, one or more virtual patients. The one or more virtual patients can be used, for example, to conduct a virtual clinical trial, such as to determine an efficacy of a therapy or medical device. The one or more virtual patients mathematically represent a physiological system in an actual patient for a health condition. Also, the one or more virtual patients can be used, for example, to determine an adverse effect from a therapy or any other deviation from a therapy, or compliance of a patient suffering from a health condition on a therapeutic protocol.