公开(公告)号：US20230248273A1

公开(公告)日：2023-08-10

申请号：US18190831

申请日：2023-03-27

Applicant: Dexcom, Inc.

Inventor： Phil MAYOU ,  Hari HAMPAPURAM ,  David A. PRICE ,  Keri J. LEONE ,  Kostyantyn SNISARENKO ,  Michael Robert MENSINGER ,  Leif N. BOWMAN ,  Robert J. BOOCK ,  Apurv Ullas KAMATH ,  Eli REIHMAN ,  Peter C. SIMPSON

IPC: A61B5/145 ,  G16H50/70 ,  G16H50/20 ,  G16H15/00

CPC classification number: A61B5/14532 ,  G16H50/70 ,  G16H50/20 ,  G16H15/00 ,  Y02A90/10

Abstract: Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.

公开(公告)号：US20240407734A1

公开(公告)日：2024-12-12

申请号：US18737588

申请日：2024-06-07

Applicant: Dexcom, Inc.

Inventor： Jee Hye PARK ,  Spencer Troy FRANK ,  David A. PRICE ,  Charles R. STROYECK ,  Arunachalam PANCH SANTHANAM ,  Joseph J. BAKER ,  Peter C. SIMPSON ,  Kazanna C. HAMES ,  Qi AN ,  Abdulrahman JBAILY ,  Justin Yi-Kai LEE ,  Stephanie Grace MOORE

IPC: A61B5/00 ,  A61B5/145 ,  G16H50/20

Abstract: A method for predicting disease is provided. The method includes generating biased analyte data by adding analyte sensor bias to historical analyte data, associating the biased analyte data with clinical disease diagnoses associated with the historical analyte data, and extracting features from the biased analyte data. The method further includes, for each model of a number of models, generating disease predictions based on different combinations of the features extracted from the biased analyte data, and evaluating the disease predictions based on the clinical disease diagnoses associated with the biased analyte data. The method further includes selecting a model and a combination of features based on a performance metric and a robustness metric.