1. WO2026174381 - SYSTEMS AND METHODS FOR PREDICTING HEART DISEASE EVENTS

Publication Number WO/2026/174381

Publication Date 27.08.2026

International Application No. PCT/CA2026/050247

International Filing Date 17.02.2026

IPC

G16H 50/30 2018.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
H	HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50	ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30	for calculating health indices; for individual health risk assessment

A61B 5/00 2006.1

A	HUMAN NECESSITIES
61	MEDICAL OR VETERINARY SCIENCE; HYGIENE
B	DIAGNOSIS; SURGERY; IDENTIFICATION
5	Measuring for diagnostic purposes ; Identification of persons

A61B 5/024 2006.1

A	HUMAN NECESSITIES
61	MEDICAL OR VETERINARY SCIENCE; HYGIENE
B	DIAGNOSIS; SURGERY; IDENTIFICATION
5	Measuring for diagnostic purposes ; Identification of persons
02	Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
024	Measuring pulse rate or heart rate

G16H 50/20 2018.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
H	HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50	ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20	for computer-aided diagnosis, e.g. based on medical expert systems

G16H 50/30 2018.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
H	HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50	ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30	for calculating health indices; for individual health risk assessment

A61B 5/00 2006.1

A	HUMAN NECESSITIES
61	MEDICAL OR VETERINARY SCIENCE; HYGIENE
B	DIAGNOSIS; SURGERY; IDENTIFICATION
5	Measuring for diagnostic purposes ; Identification of persons

A61B 5/024 2006.1

A	HUMAN NECESSITIES
61	MEDICAL OR VETERINARY SCIENCE; HYGIENE
B	DIAGNOSIS; SURGERY; IDENTIFICATION
5	Measuring for diagnostic purposes ; Identification of persons
02	Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
024	Measuring pulse rate or heart rate

G16H 50/20 2018.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
H	HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50	ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20	for computer-aided diagnosis, e.g. based on medical expert systems

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Applicants

• UNIVERSITY HEALTH NETWORK [CA]/[CA]

Inventors

• ROSS, Heather
• MCINTOSH, Christopher
• DUHAMEL, Joseph
• MOAYEDI, Yasbanoo
• SIMARD, Ann Marie
• BRAHMBHATT, Darshan
• FOROUTAN, Farid
• GAO, Yuan
• BRUM, Margaret
• DE LUCA, Enza
• KIM, Ben
• VERMA, Bhavish

Agents

• NORTON ROSE FULBRIGHT CANADA S.E.N.C.R.L., S.R.L. / LLP

Priority Data

63/759,824

18.02.2025

US

Publication Language English (en)

Filing Language English (en)

Designated States

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AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CV, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IQ, IR, IS, IT, JM, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, MG, MK, MN, MU, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UY, UZ, VC, VN, WS, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, CV, GH, GM, KE, LR, LS, MU, MW, MZ, NA, RW, SC, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Organization (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, ME, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)

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Title

(EN) SYSTEMS AND METHODS FOR PREDICTING HEART DISEASE EVENTS

(FR) SYSTÈMES ET PROCÉDÉS DE PRÉDICTION D'ÉVÉNEMENTS DE MALADIE CARDIAQUE

Abstract

(EN) Systems, methods, and computer-readable media are disclosed for predicting heart disease events using multimodal biometric and clinical data processed by a temporally-aware machine learning model. Biometric data, such as sensor measurements and additional wearable-derived activity and physiological signals, are collected from a user over a multi-day collection time period (e.g., 10–30 days). Biometric features generated from the collected data are supplemented with clinical features such as demographic and drug-use information. The combined feature set is provided to a machine learning model comprising a sequence of transformer blocks configured to generate progressively lower-resolution temporal representations through pooling and to enforce temporal causality via causal self-attention. The model produces an output predictive of a heart disease event, which may include a metric of VO₂ regression or other indicators of cardiopulmonary decline.

(FR) Des systèmes, des procédés et des supports lisibles par ordinateur sont divulgués, qui sont destinés à prédire des événements de maladie cardiaque à l'aide de données biométriques et cliniques multimodales traitées par un modèle d'apprentissage automatique sensible au temps. Des données biométriques, telles que des mesures de capteur et des signaux supplémentaires d'activité et physiologiques issus de dispositifs habitroniques, sont collectées auprès d'un utilisateur sur une période de temps de collecte de plusieurs jours (par exemple, de 10 à 30 jours). Des caractéristiques biométriques générées à partir des données collectées sont complétées par des caractéristiques cliniques telles que des informations démographiques et de consommation de médicaments. L'ensemble de caractéristiques combinées est fourni à un modèle d'apprentissage automatique comprenant une séquence de blocs transformeurs configurés pour générer des représentations à résolution temporelle progressivement décroissante par sous-échantillonnage et pour appliquer une causalité temporelle par l'intermédiaire d'une auto-attention causale. Le modèle produit une sortie prédictive d'un événement de maladie cardiaque, qui peut comprendre une métrique de régression VO₂ ou d'autres indicateurs de déclin cardiopulmonaire.

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