Analyte sensor data evaluation and error reduction apparatus and methods

Abstract

Apparatus and methods for error modeling and correction in a blood analyte sensor or system. In one exemplary embodiment, the apparatus employs: (i) a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the blood analyte measurement process, and (ii) generation of an operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation and collection of blood analyte data. This enhances device signal stability and accuracy over extended periods, thereby enabling among other things extended periods of blood analyte sensor implantation, and “personalization” of the sensor apparatus to each user receiving an implant. In one variant, the blood analyte is glucose, and the implanted sensor utilizes an oxygen-based molecular measurement principle.

1 .- 20 . (canceled) 
     
     
         21 . A method of monitoring a blood analyte level within a living being using a blood analyte sensing apparatus, the method comprising:
 calibrating the blood analyte sensing apparatus using one or more predetermined calibration models;   operating the calibrated blood analyte sensing apparatus in a training mode;   based at least in part on the operating in the training mode, generating an error correction operational model; and   subsequent to generating the error correction operational model, operating the blood analyte sensing apparatus in a detection mode, the operating in the detection mode including applying the error correction operational model on at least a portion of current blood analyte signal data.   
     
     
         22 . The method of  claim 21 , wherein the operating the blood analyte sensing apparatus in the training mode comprises: (i) receiving time-stamped blood analyte reference data; and (ii) collecting time-stamped calculated blood analyte sensor data. 
     
     
         23 . The method of  claim 22 , wherein the operating the blood analyte sensing apparatus in the training mode further comprises collecting time-stamped data derived from one or more in vivo ancillary sensors. 
     
     
         24 . The method of  claim 23 , wherein the collecting the time-stamped data derived from one or more in vivo ancillary sensors comprises collecting data from one or more of a temperature sensor, a pressure sensor, or a motion sensor. 
     
     
         25 . The method of  claim 22 , wherein:
 the collecting the time-stamped calculated blood analyte sensor data comprises collecting blood analyte sensor data subject to one or more unmodeled systematic errors; and   the receiving the time-stamped blood analyte reference data comprises receiving blood analyte data not subject to the one or more unmodeled systematic errors.   
     
     
         26 . The method of  claim 22 , wherein the generating the error correction operation model comprises generating blood analyte error data by at least calculating a blood analyte error value at one or more of a plurality of time points corresponding to respective one or more the time-stamped blood analyte reference data. 
     
     
         27 . The method of  claim 26 , wherein the generating of the error correction operation model comprises identifying one or more parameters that have a prescribed level of correlation to the blood analyte error data. 
     
     
         28 . The method of  claim 27 , wherein the generating of the error correction operation model further comprises applying one or more mathematical models to at least (i) the blood analyte error data, and (ii) data relating to the identified one or more parameters. 
     
     
         29 . The method of  claim 21 , wherein the generating of the error correction operation model comprises using at least one computerized machine learning process to generate one or more user-specific models. 
     
     
         30 . The method of  claim 29 , wherein the using at least one computerized machine learning process to generate the one or more user-specific models comprises using the at least one computerized machine learning process without input data indicative of an identification of one or more physiological mechanisms as sources of blood analyte error. 
     
     
         31 . The method of  claim 21 , further comprising:
 generating a second error correction operational model;   substituting the generated second error correction operational model for the error correction operational model; and   using the generated second error correction operation model to process at least a portion of blood analyte signal data generated subsequent to the substituting.   
     
     
         32 . A computerized apparatus for monitoring a blood analyte level, the computerized apparatus comprising:
 data processing apparatus configured for data communication with an analyte sensor apparatus implanted within a body of a subject;   a storage apparatus in data communication with the data processing apparatus and comprising a storage medium having at least one computer program stored thereon, the at least one computer program comprising a plurality of instructions which are configured to, when executed on the data processing apparatus:   (i) cause initial calibration of the analyte sensor apparatus;   (ii) cause the initially calibrated analyte sensor apparatus to operate in a first mode;   (iii) generate an error correction operational model, the error correction operational model based at least in part on the operation of the initially calibrated analyte sensor in the first mode; and   (iv) cause operation of the analyte sensor apparatus in a second mode, the operation in the second mode comprising at least application of the error correction operational model to blood analyte signal data processed subsequent to entry of the analyte sensor apparatus into the second mode of operation.   
     
     
         33 . The computerized apparatus of  claim 32 , wherein:
 the data processing apparatus is disposed on a receiver apparatus external to the body of the subject; and   wherein the generation of the error correction operation model comprises transmission of data collected via the analyte sensor apparatus during operation in the first mode, the transmission via a wireless interface of the analyte sensor apparatus to the receiver apparatus, the receiver apparatus configured to perform or cause performance of the generation of the model; and   wherein the application of the error correction model on the blood analyte signal data comprises application of model data received via the wireless interface from the receiver.   
     
     
         34 . The computerized apparatus of  claim 32 , wherein:
 the analyte sensor apparatus comprises an oxygen-based blood glucose sensor;   the blood analyte signal data comprises blood glucose signal data; and   the processing apparatus and the storage apparatus are disposed on or integral with the analyte sensor apparatus in an implantable form factor.   
     
     
         35 . The computerized apparatus of  claim 32 , wherein the generation of the error correction operation model comprises:
 calculation of data related to a plurality of blood analyte error values; and   utilization of one or more machine learning algorithms on (i) at least a portion of the data related to the plurality of error values, and (ii) data related to one or more candidate parameters, to model one or more previously unmodeled error sources related to the implantation of the blood analyte sensor within the subject.   
     
     
         36 . A method of operating an implanted blood analyte sensor within a living being, the method comprising:
 obtaining first blood analyte data using the blood analyte sensor, at least a portion of the first blood analyte data subject to one or more unmodeled systematic sources of error;   obtaining reference data not subject to the one or more unmodeled systematic sources of error;   calculating a plurality of blood analyte error values using the first blood analyte data and the corresponding reference data;   identifying one or more parameters that have at least one of a prescribed level or prescribed type of correlation to the blood analyte error values;   generating an error correction model based at least on the plurality of blood analyte error values, data relating to the identified one or more parameters, and one or more mathematical models; and   obtaining calibrated blood analyte data by at least applying the error correction model to then-current blood analyte data.   
     
     
         37 . The method of  claim 36 , wherein the calculating the plurality of blood analyte error values comprises at least associating the first blood analyte data and the corresponding reference data at each of a plurality of respective time coordinates. 
     
     
         38 . The method of  claim 36 , wherein the identifying the one or more parameters comprises:
 identifying one or more candidate parameters;   associating data related to the one or more candidate parameters with the plurality of blood analyte error values; and   identifying an individual one or ones of the one or more candidate parameters meeting the at least one of the prescribed level or prescribed type of correlation to the plurality of error values.   
     
     
         39 . The method of  claim 36 , wherein the generating of the error correction model comprises utilizing one or more machine learning algorithms on at least a portion of the plurality of blood analyte error values. 
     
     
         40 . The method of  claim 39 , wherein the utilizing the one or more machine learning algorithms comprises:
 utilizing a first machine learning algorithm to generate a first error model data set;   utilizing a second machine learning algorithm to generate a second error model data set;   evaluating the first error model set and the second error model set; and   based at least on the evaluating, selecting one of the first machine learning algorithm or the second machine learning algorithm for the generating of the operational error correction model.