Methods and systems for training medical machine-learning models
WO2024264015A3 Publication Date: 2025-03-27POLYVIEW HEALTH INC
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Patent Information
- Application Number
- PCT/US2024/035157
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2023-12-01
- Filing Date
- 2024-06-23
- Publication Date
- 2025-03-27
AI Technical Summary
Technical Problem
Current telehealth communication sessions are inefficient and disengaging, leading to reduced trust and comfort for patients, making it difficult for healthcare providers to effectively communicate and provide relevant information.
Method used
Training machine-learning models on real-time communications by processing and analyzing interactions between healthcare providers and patients, using natural language models to extract features, generate training datasets, and train models to improve communication efficacy and patient engagement.
Benefits of technology
Enhances the efficiency and engagement of telehealth sessions by providing insights and analytics that improve communication, patient trust, and healthcare provider effectiveness.
✦ Generated by Eureka AI based on patent content.
Abstract
A generative machine-learning model may be trained using communication session data to tailor a generative model on particular classes of communications. A set of communications sessions may be received with each communication session including, for example, communications between a doctor and a patient. A set of features may be generated by a natural language model from communications extracted from the communication sessions. A subset of the set of communication sessions may be defined based on the set of features. A training dataset may be defined using the subset of the set of communication sessions and used to train a machine-learning model. The machine-learning model can be configured to generate one or more contexts associated with a feature of the set of features. Upon receiving a request associated with a particular, a response may be generated using the machine-learning model and a feature vector derived from the request.
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