Koninklijke Philips had 239 patents in artificial intelligence during Q2 2024. The patents filed by Koninklijke Philips NV in Q2 2024 focus on methods and systems for predicting patient dropout from remote patient monitoring programs, monitoring fetal heartbeat using ultrasound technology, adapting machine learning models for physiological status monitoring, and a sleep monitoring system using a stochastic neural network to estimate sleep parameters and uncertainty. These innovations aim to improve patient care, outcomes, and experience through data-driven approaches and targeted interventions. GlobalData’s report on Koninklijke Philips gives a 360-degree view of the company including its patenting strategy. Buy the report here.
Koninklijke Philips had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Intelligent drop-out prediction in remote patient monitoring (Patent ID: US20240212849A1)
The patent by Koninklijke Philips NV focuses on methods and systems for predicting patient dropout from remote patient monitoring programs using a data-driven approach. By analyzing clinical features, a dropout prediction engine generates a dropout risk score for patients and identifies potential dropout causes. The system then recommends engagement actions to prevent patient dropout, ultimately leading to improved patient engagement, lower costs of care, and better health outcomes.
The method and system outlined in the patent involve obtaining medical records, extracting dropout prediction features, generating dropout risk scores, and determining potential dropout causes and recommended engagement actions. The dropout prediction engine utilizes a trained model based on historical patient data, while the engagement recommendation engine evaluates feature contributions using a Shapley values algorithm. The system considers various patient information, such as medical history, treatment records, physiological measurements, and feedback data, to predict dropout risk accurately. Additionally, the system can determine patient phenotypes and recommend engagement actions based on these subgroups, ultimately aiming to prevent patient dropout and ensure continued participation in remote monitoring programs.
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