Sensor measurement value calibration using sensor calibration data and a performance model

1. A processor-implemented method, comprising:

obtaining one or more electrical signals from a sensing element of a sensing arrangement, wherein the one or more electrical signals are influenced by a physiological condition in a body of a patient, and wherein the sensing element is at least partially inserted into the body of the patient;

obtaining, from a data storage element of the sensing arrangement, calibration data associated with the sensing element, wherein the calibration data associated with the sensing element is determined based on estimated fabrication process measurement data for the sensing element and a calibration model that predicts output measurement parameters of the sensing element for a certain physiological condition as a function of fabrication process measurement parameters of the sensing element;

converting, using the calibration data, the one or more electrical signals into one or more calibrated measurement parameters;

obtaining a performance model associated with the sensing element;

obtaining personal data associated with the patient; and

determining, using the performance model and based on the personal data and the one or more calibrated measurement parameters, a calibrated output value indicative of the physiological condition.

2. The processor-implemented method of claim 1 , wherein the calibration data associated with the sensing element includes one or more calibration factors or scaling factors for converting the one or more electrical signals into the one or more calibrated measurement parameters.

3. The processor-implemented method of claim 1 , further comprising:

obtaining fabrication process measurement data for a substrate having the sensing element fabricated thereon;

obtaining the calibration model; and

determining, based on the fabrication process measurement data and using the calibration model, the calibration data associated with the sensing element for converting the one or more electrical signals into the one or more calibrated measurement parameters, wherein the calibration data is stored in the data storage element of the sensing arrangement.

4. The processor-implemented method of claim 3 , wherein obtaining the calibration model comprises:

obtaining fabrication process measurement parameters for a plurality of instances of the sensing element;

obtaining output measurement parameters of the plurality of instances of the sensing element under a reference physiological condition; and

determining the calibration model based on the fabrication process measurement parameters and the output measurement parameters of the plurality of instances of the sensing element under the reference physiological condition.

5. The processor-implemented method of claim 3 , wherein:

obtaining the fabrication process measurement data comprises:

obtaining a plurality of fabrication process measurements from a plurality of process control monitor (PCM) regions on the substrate; and

determining representative fabrication process measurement data associated with the sensing element based on a location of the sensing element on the substrate relative to the plurality of PCM regions; and

determining the calibration data associated with the sensing element comprises calculating the calibration data based on the representative fabrication process measurement data by inputting the representative fabrication process measurement data to the calibration model.

6. The processor-implemented method of claim 1 , wherein the performance model maps the one or more calibrated measurement parameters into the calibrated output value indicative of the physiological condition based on the personal data.

7. The processor-implemented method of claim 1 , wherein the personal data includes at least one of an age, gender, body mass index, height, weight, or demographic information associated with the patient.

8. The processor-implemented method of claim 1 , wherein:

the sensing element comprises an interstitial glucose sensing element; and

determining the calibrated output value comprises determining, using the performance model, a calibrated sensor glucose measurement value based on the personal data and the one or more calibrated measurement parameters.

9. The processor-implemented method of claim 8 , wherein the one or more calibrated measurement parameters comprise:

a calibrated measurement value of an output electrical current of the interstitial glucose sensing element;

a calibrated measurement value of an electrochemical impedance spectroscopy (EIS) of the interstitial glucose sensing element;

a calibrated value characterizing an impedance of the interstitial glucose sensing element based on the one or more electrical signals; or

a combination thereof.

10. The processor-implemented method of claim 8 , further comprising:

obtaining a plurality of historical reference blood glucose measurements for a plurality of patients;

obtaining a plurality of historical calibrated measurement parameter values for the plurality of patients corresponding to the plurality of historical reference blood glucose measurements;

obtaining patient data associated with the plurality of patients; and

determining the performance model associated with the sensing element based on relationships between the patient data, the plurality of historical calibrated measurement parameter values, and the plurality of historical reference blood glucose measurements.

11. A system comprising:

one or more processors; and

one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of:

obtaining one or more electrical signals from a sensing element of a sensing arrangement, wherein the one or more electrical signals are influenced by a physiological condition in a body of a patient, and wherein the sensing element is at least partially inserted into the body of the patient;

obtaining, from a data storage element of the sensing arrangement, calibration data associated with the sensing element, wherein the calibration data associated with the sensing element is determined based on estimated fabrication process measurement data for the sensing element and a calibration model that predicts output measurement parameters of the sensing element for a certain physiological condition as a function of fabrication process measurement parameters of the sensing element;

converting, using the calibration data, the one or more electrical signals into one or more calibrated measurement parameters;

obtaining a performance model associated with the sensing element;

obtaining personal data associated with the patient; and

determining, using the performance model and based on the personal data and the one or more calibrated measurement parameters, a calibrated output value indicative of the physiological condition.

12. The system of claim 11 , wherein the calibration data associated with the sensing element includes one or more calibration factors or scaling factors for converting the one or more electrical signals into the one or more calibrated measurement parameters.

13. The system of claim 11 , wherein the instructions, when executed by the one or more processors, further cause performance of:

obtaining fabrication process measurement data for a substrate having the sensing element fabricated thereon;

obtaining the calibration model; and

determining, based on the fabrication process measurement data and using the calibration model, the calibration data associated with the sensing element for converting the one or more electrical signals into the one or more calibrated measurement parameters, wherein the calibration data is stored in the data storage element of the sensing arrangement.

14. The system of claim 11 , wherein the performance model maps the one or more calibrated measurement parameters into the calibrated output value indicative of the physiological condition based on the personal data.

15. The system of claim 11 , wherein:

the sensing element comprises an interstitial glucose sensing element; and

determining the calibrated output value comprises determining, using the performance model, a calibrated sensor glucose measurement value based on the personal data and the one or more calibrated measurement parameters.

16. One or more non-transitory processor-readable media storing instructions which, when executed by one or more processors, cause performance of:

obtaining one or more electrical signals from a sensing element of a sensing arrangement, wherein the one or more electrical signals are influenced by a physiological condition in a body of a patient, and wherein the sensing element is at least partially inserted into the body of the patient;

obtaining, from a data storage element of the sensing arrangement, calibration data associated with the sensing element, wherein the calibration data associated with the sensing element is determined based on estimated fabrication process measurement data for the sensing element and a calibration model that predicts output measurement parameters of the sensing element for a certain physiological condition as a function of fabrication process measurement parameters of the sensing element;

converting, using the calibration data, the one or more electrical signals into one or more calibrated measurement parameters;

obtaining a performance model associated with the sensing element;

obtaining personal data associated with the patient; and

determining, using the performance model and based on the personal data and the one or more calibrated measurement parameters, a calibrated output value indicative of the physiological condition.

17. The one or more non-transitory processor-readable media of claim 16 , wherein the calibration data associated with the sensing element includes one or more calibration factors or scaling factors for converting the one or more electrical signals into the one or more calibrated measurement parameters.

18. The one or more non-transitory processor-readable media of claim 16 , wherein the performance model maps the one or more calibrated measurement parameters into the calibrated output value indicative of the physiological condition based on the personal data.