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1. WO2020094765 - METHOD AND SYSTEM OF DETERMINING A PROBABILITY OF A BLOOD GLUCOSE VALUE FOR A PATIENT BEING IN AN ADVERSE BLOOD GLUCOSE RANGE AT A PREDICTION TIME, AND COMPUTER PROGRAM PRODUCT

Publication Number WO/2020/094765
Publication Date 14.05.2020
International Application No. PCT/EP2019/080486
International Filing Date 07.11.2019
IPC
G16H 50/30 2018.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30for calculating health indices; for individual health risk assessment
G16H 50/70 2018.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
70for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 2018.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
G06K 9/62 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
CPC
G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30for calculating health indices; for individual health risk assessment
G16H 50/70
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
70for mining of medical data, e.g. analysing previous cases of other patients
Applicants
  • F. HOFFMANN-LA ROCHE AG [CH]/[CH] (AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BE, BF, BG, BH, BJ, BN, BR, BW, BY, BZ, CA, CF, CG, CH, CI, CL, CM, CN, CO, CR, CU, CY, CZ, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, FR, GA, GB, GD, GE, GH, GM, GN, GQ, GR, GT, GW, HN, HR, HU, ID, IE, IL, IN, IR, IS, IT, JO, JP, KE, KG, KH, KM, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LT, LU, LV, LY, MA, MC, MD, ME, MG, MK, ML, MN, MR, MT, MW, MX, MY, MZ, NA, NE, NG, NI, NL, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SI, SK, SL, SM, SN, ST, SV, SY, SZ, TD, TG, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, UZ, VC, VN, ZA, ZM, ZW)
  • ROCHE DIABETES CARE GMBH [DE]/[DE] (DE)
  • ROCHE DIABETES CARE, INC. [US]/[US] (US)
  • MYSUGR [AT]/[AT]
Inventors
  • DUKE, David L.
  • BANKOSEGGER, Rafael
  • WREDE, Jan
Agents
  • MEINEL, Anne Julia
Priority Data
18209230.429.11.2018EP
62/75663007.11.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) METHOD AND SYSTEM OF DETERMINING A PROBABILITY OF A BLOOD GLUCOSE VALUE FOR A PATIENT BEING IN AN ADVERSE BLOOD GLUCOSE RANGE AT A PREDICTION TIME, AND COMPUTER PROGRAM PRODUCT
(FR) PROCÉDÉ ET SYSTÈME DE DÉTERMINATION D'UNE PROBABILITÉ D'UNE VALEUR DE GLYCÉMIE D’UN PATIENT COMPRISE DANS UNE PLAGE DE GLYCÉMIE DÉFAVORABLE À UN INSTANT DE PRÉDICTION ET PRODUIT-PROGRAMME INFORMATIQUE
Abstract
(EN)
The disclosure refers to a method of determining a probability of a blood glucose value for a patient being in an adverse blood glucose range at a prediction time, the method, in a system (10) having one or more data processors, comprising: providing spot monitoring blood glucose measurement data representing a plurality of blood glucose measurement values for a measurement time period, the spot monitoring blood glucose measurement data comprising respective measurement times at which measurements for the blood glucose measurement values have been conducted, wherein the blood glucose measurement values comprise first blood glucose measurement values assigned to a first adverse range of blood glucose values, and second blood glucose measurement values assigned to a second adverse range of blood glucose values, wherein the second range of blood glucose values is different from the first adverse range of blood glucose values; by applying an analysis algorithm comprising a kernel density estimation and Bayes' rule, determining from the spot monitoring blood glucose measurement data comprising the first blood glucose measurement values and the second blood glucose measurement values a probability of the blood glucose value of a patient being in the first adverse blood glucose range at a prediction time, and the blood glucose value of the patient being in the second adverse blood glucose range at the prediction time; wherein, in the kernel density estimation, a first kernel bandwidth is applied for all or some of the first blood glucose measurement values and a second kernel bandwidth different from the first kernel bandwidth is applied for all or some the second blood glucose measurement values; and providing output data indicative of the prediction time and the probability value at the prediction time. Further, a system and a computer program product are provided.
(FR)
La présente invention concerne un procédé de détermination d'une probabilité d'une valeur de glycémie pour un patient comprise dans une plage de glycémie défavorable à un instant de prédiction. Ledit procédé, dans un système (10) doté d’un ou de plusieurs processeurs de données, comprend les étapes suivantes : fournir des données de mesure de glycémie de contrôle aléatoire représentant une pluralité de valeurs de mesure de glycémie pour une période de temps de mesure, les données de mesure de glycémie de contrôle aléatoire comprenant des moments de mesure respectifs auxquels des mesures correspondant aux valeurs de mesure de glycémie ont été effectuées, les valeurs de mesure de glycémie comprenant des premières valeurs de mesure de glycémie attribuées à une première plage défavorable de valeurs de glycémie, et des secondes valeurs de mesure de glycémie attribuées à une seconde plage défavorable de valeurs de glycémie, la seconde plage de valeurs de glycémie étant différente de la première plage défavorable de valeurs de glycémie ; en appliquant un algorithme d'analyse comprenant une estimation de densité de noyau et la règle de Bayes, déterminer, à partir des données de mesure de glycémie de contrôle aléatoire comprenant les premières valeurs de mesure de glycémie et les secondes valeurs de mesure de glycémie, une probabilité de la valeur de glycémie d'un patient comprise dans la première plage de glycémie défavorable à un instant de prédiction, et la valeur de glycémie du patient comprise dans la seconde plage de glycémie défavorable au moment de la prédiction ; dans l'estimation de densité de noyau, une première bande passante de noyau est appliquée pour la totalité ou une partie des premières valeurs de mesure de glycémie et une seconde bande passante de noyau différente de la première bande passante de noyau est appliquée pour la totalité ou une partie des secondes valeurs de mesure de glycémie ; et fournir des données de sortie indicatives du temps de prédiction et de la valeur de probabilité à l’instant de prédiction. De plus, l'invention concerne également un produit-programme informatique.
Also published as
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