Processing

Please wait...

Settings

Settings

Goto Application

1. WO2020089576 - METHOD AND APPARATUS FOR MONITORING A PATIENT

Publication Number WO/2020/089576
Publication Date 07.05.2020
International Application No. PCT/GB2019/052662
International Filing Date 23.09.2019
IPC
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
G06N 3/02 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
CPC
G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines [SVM]
G06N 20/20
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
20Ensemble learning
G06N 3/0445
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0445Feedback networks, e.g. hopfield nets, associative networks
G06N 3/0454
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
0454using a combination of multiple neural nets
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06N 5/003
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
003Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
Applicants
  • OXFORD UNIVERSITY INNOVATION LIMITED [GB]/[GB]
Inventors
  • CLIFTON, David
  • ZHU, Tingting
  • TAYLOR, Thomas
  • JAVED, Hamza
  • EL-BOURI, Rasheed
  • DUNN, Iain
  • WATKINSON, Peter
  • BISHOP, Jennifer
Agents
  • J A KEMP LLP
Priority Data
1817708.930.10.2018GB
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) METHOD AND APPARATUS FOR MONITORING A PATIENT
(FR) PROCÉDÉ ET APPAREIL DE SUIVI D'UN PATIENT
Abstract
(EN)
Methods and apparatus for monitoring a patient are provided. In one arrangement, a multi- dimensional patient data set is received at each of a plurality of different reference times. Each dimension of the patient data set stores a value representing a different type of information about the patient. A plurality of predictions of a health trajectory of the patient are generated. Each prediction is generated using a trained machine learning model receiving as input a different one of the patient data sets. The trained machine learning model may be dimensionally adaptive, such that predictions of the patient trajectories are provided using patient data sets having different respective dimensionalities for at least a sub-set of the reference times. The trained machine learning model may use machine learned predictions of accuracy to select trained machine learning units from an ensemble of trained machine learning units.
(FR)
L’invention concerne des procédés et un appareil de suivi d'un patient. Dans un agencement, un ensemble de données de patient multidimensionnel est reçu à chacune d'une pluralité de temps de référence différents. Chaque dimension de l'ensemble de données de patient stocke une valeur représentant un type différent d'informations concernant le patient. Une pluralité de prédictions d'une évolution de la santé du patient sont générées. Chaque prédiction est générée à l'aide d'un modèle d'apprentissage machine entraîné recevant en entrée un ensemble différent des ensembles de données de patient. Le modèle d'apprentissage machine entraîné peut être adaptatif dimensionnellement, de telle sorte que des prédictions des trajectoires de patient sont fournies à l'aide d'ensembles de données de patient ayant différentes dimensionnalités respectives pour au moins un sous-ensemble des temps de référence. Le modèle d'apprentissage machine entraîné peut utiliser des prédictions de précision apprises par machine pour sélectionner des unités d'apprentissage machine entraînées à partir d'un ensemble d'unités d'apprentissage machine entraînées.
Latest bibliographic data on file with the International Bureau