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1. WO2019055945 - COURSE OF TREATMENT RECOMMENDATION SYSTEM

Publication Number WO/2019/055945
Publication Date 21.03.2019
International Application No. PCT/US2018/051388
International Filing Date 17.09.2018
IPC
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
G16B 40/00 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 25/00 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
25ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
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 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 7/005
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computer systems based on specific mathematical models
005Probabilistic networks
G16B 20/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 40/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
G16H 10/60
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
10ICT specially adapted for the handling or processing of patient-related medical or healthcare data
60for patient-specific data, e.g. for electronic patient records
Applicants
  • SKELLENGER, John Scott [US]/[US]
Inventors
  • SKELLENGER, John Scott
Agents
  • PIRIO, Maurice J.
  • SUMEDHA, Ahuja
  • AI, Bing
  • ARNETT, Stephen, E.
  • ASPHAHANI, Fareid
Priority Data
62/560,12818.09.2017US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) COURSE OF TREATMENT RECOMMENDATION SYSTEM
(FR) SYSTÈME DE RECOMMANDATION D'UNE SÉRIE DE TRAITEMENTS
Abstract
(EN)
A system for generating a course of treatment ("COT") recommender for recommending COTs for patients using machine learning is provided. A machine learning treatment recommendation ("MLTR") system trains a COT recommender using training data that includes a feature vector and a label for each patient in a group of patients. The features of the feature vector may include features derived from patient data. A label is a course of treatment for a patient referred to as a labeling course of treatment. The MLTR system generates the training data from patient data collected over time. The MLTR system then uses the training data to train the COT recommender using a machine learning technique. Once the COT recommender has been trained, the COT recommender can be applied to a feature vector of patient data of a patient to generate an MLTR recommended course of treatment for the patient.
(FR)
L'invention concerne un système pour générer un dispositif de recommandation d'une série de traitements (« COT ») permettant de recommander des COT pour des patients à l'aide d'un apprentissage automatique. Un système de recommandation d'une série de traitements à apprentissage automatique (« MLTR ») entraîne un dispositif de recommandation de COT à l'aide de données d'apprentissage qui comprennent un vecteur de caractéristiques et un marqueur pour chaque patient dans un groupe de patients. Les caractéristiques du vecteur de caractéristiques peuvent comprendre des caractéristiques dérivées des données du patient. Un marqueur est une série de traitements pour un patient désignée comme série de traitements de marquage. Le système de MLTR génère les données d'apprentissage à partir des données de patient collectées au cours du temps. Le système de MLTR utilise ensuite les données d'apprentissage pour entraîner le dispositif de recommandation de COT à l'aide d'une technique d'apprentissage automatique. Une fois que le dispositif de recommandation de COT a été entraîné, le dispositif de recommandation de COT peut être appliqué à un vecteur de caractéristiques de données de patient d'un patient pour générer une série de traitements recommandée par MLTR pour le patient.
Latest bibliographic data on file with the International Bureau