DETECTION OF CHANGES IN PATIENT HEALTH BASED ON GLUCOSE DATA
개요
발명자
Kamal Deep Mothilal; Michael D. Eggen; Ning Yu; John P. Keane; Shantanu Sarkar; Randal C. Schulhauser; David L. Probst; Mark R. Boone; Kenneth A. Timmerman; Stanley J. Taraszewski; Matthew A. Joyce; Amruta Paritosh Dixit; Kathryn Hilpisch; Kathryn Ann Milbrandt; Laura M. Zimmerman; Matthew L. Plante
IPC 분류
CPC 분류
This disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.
원문 (중국어)
This disclosure is directed to systems and techniques for detecting change in patient health based upon patient data. In one example, a medical system comprising processing circuitry communicably coupled to a glucose sensor and configured to generate continuous glucose sensor measurements of a patient. The processing circuitry is further configured to: extract at least one feature from the continuous glucose sensor measurements over at least one time period, wherein the at least one feature comprises one or more of an amount of time within a pre-determined glucose level range, a number of hypoglycemia events, a number of hyperglycemia events, or one or more statistical metrics corresponding to the continuous glucose sensor measurements; apply a machine learning model to the at least one extracted feature to produce data indicative of a risk of a cardiovascular event; and generate output data based on the risk of the cardiovascular event.