Electrical Computing Devices Providing Personalized Patient Drug Dosing Regimens

1 . A method comprising:

receiving, from a non-transitory computer-readable medium, a dosing model for a substance;

receiving, from a remote device via a communications network, dosing information for a patient for the substance;

updating, by one or more electronic processors, the dosing model based at least in part on the dosing information;

executing, by one of the one or more electronic processors, a predictive model based on the updated dosing model to generate a dosing recommendation; and

providing, to the remote device via the communications network, the dosing recommendation.

2 . The method of claim 1 , wherein the dosing model comprises a non-linear mixed effects model.

3 . The method of claim 1 , wherein the predictive model comprises a Bayesian network.

4 . The method of claim 1 , wherein the dosing information comprises a patient characteristic and at least one of a dose amount, a dosing interval, or an exposure level.

5 . The method of claim 4 , wherein the dosing information further comprises at least two of a dose amount, a dosing interval, or an exposure level, and further comprising determining the one of the dose amount, the dosing interval, or the exposure level that was not received.

6 . The method of claim 1 , wherein the dosing recommendation comprises at least one of a dose amount, a dosing interval, or an exposure level.

7 . The method of claim 1 , further comprising:

receiving, via the communications network, second dosing information for a second patient for the substance;

determining, by one of the one or more electronic processors, whether the second dosing information may be used for a dosing model update based on demographic requirements for the dosing model;

responsive to determining the second dosing information may not be used for the dosing model update:

not updating the dosing model based on the second dosing information;

executing, by one of the one or more electronic processors, a predictive model based on the unmodified dosing model to generate a dosing recommendation; and

providing, via the communications network, the dosing recommendation.

8 . The method of claim 7 , wherein the demographic requirements comprise at least one of a country of residence, a country of treatment, a hospital, or a provider.

9 . The method of claim 1 , wherein updating the dosing model is performed in response to receiving the dosing information and wherein the dosing recommendation is for the patient.

10 . The method of claim 1 , wherein the dosing recommendation is for a second patient different from the patient.

11 . The method of claim 1 , wherein updating the dosing model is performed after a predetermined time period or after receiving a predetermined amount of dosing information.

12 . The method of claim 1 , further comprising iteratively:

receiving, via the communications network, additional dosing information for one or more patients; and

updating the dosing model based on the additional dosing information.

13 . The method of claim 1 , further comprising generating visualization information based on the dosing model and the predictive model, and transmitting the visualization information to the remote device via the communications network.

14 . The method of claim 13 , wherein the visualization information comprises blood concentration associated with the substance.

15 . The method of claim 13 , wherein the visualization information comprises statistical information associated with use of the substance for a demographic population, wherein the patient is a member of the demographic population.

16 . A system comprising:

a network interface;

a non-transitory computer-readable medium; and

a processor in communication with the network interface and the non-transitory computer-readable medium, the processor configured to:

receive a dosing model for a substance;

receive, from a remote device via the network interface, dosing information for a patient for the substance;

update the dosing model based at least in part on the dosing information;

execute a predictive model based on the updated dosing model to generate a dosing recommendation; and

provide the dosing recommendation to the remote device via the network interface.

17 . The system of claim 16 , wherein the dosing model comprises a non-linear mixed effects model.

18 . The system of claim 16 , wherein the predictive model comprises a Bayesian network.

19 . The system of claim 16 , further comprising a data store, and wherein the data store comprises a plurality of data records, the data records comprising historical dosing information, and wherein the processor is further configured to store the dosing information in a data record in the data store, and update the dosing model based on data records in the data store.

20 . The system of claim 16 , wherein the dosing information comprises a patient characteristic and at least one of a dose amount, a dosing interval, or an exposure level.

21 . The system of claim 20 , wherein the dosing information further comprises at least two of a dose amount, a dosing interval, or an exposure level, and wherein the processor is configured to determine the one of the dose amount, the dosing interval, or the exposure level that was not received.

22 . The system of 16 , wherein the dosing recommendation comprises at least one of a dose amount, a dosing interval, or an exposure level

23 . The system of claim 16 , further comprising generating visualization information based on the dosing model and the predictive model, and transmitting the visualization information to the remote device via the network interface.

24 . The system of claim 23 , wherein the visualization information comprises blood concentration associated with the substance.

25 . The method of claim 23 , wherein the visualization information comprises statistical information associated with use of the substance for a demographic population, wherein the patient is a member of the demographic population.