18463673. MACHINE LEARNING METHOD FOR PREDICTING A HEALTH OUTCOME OF A PATIENT USING VIDEO AND AUDIO ANALYTICS simplified abstract (Insight Direct USA, Inc.)

MACHINE LEARNING METHOD FOR PREDICTING A HEALTH OUTCOME OF A PATIENT USING VIDEO AND AUDIO ANALYTICS

Organization Name

Insight Direct USA, Inc.

Inventor(s)

Michael Griffin of Wayland MA (US)

Hailey Kotvis of Wauwatosa WI (US)

Josephine Miner of Hope RI (US)

Porter Moody of Wayland MA (US)

Kayla Poulsen of Natick MA (US)

Austin Malmin of Gilbert AZ (US)

Sarah Onstad-hawes of Seattle WA (US)

Gloria Solovey of Arlington MA (US)

Austin Streitmatter of Palm Harbor FL (US)

MACHINE LEARNING METHOD FOR PREDICTING A HEALTH OUTCOME OF A PATIENT USING VIDEO AND AUDIO ANALYTICS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18463673 titled 'MACHINE LEARNING METHOD FOR PREDICTING A HEALTH OUTCOME OF A PATIENT USING VIDEO AND AUDIO ANALYTICS

Simplified Explanation

The patent application describes a method and apparatus for predicting a health outcome of a patient using a machine learning model operating on video, audio, and text data extracted from a video stream of the patient.

• Video, audio, and text data are extracted from a video stream of the patient.
• The video data are analyzed to identify a first feature set.
• The audio data are analyzed to identify a second feature set.
• The text data are analyzed to identify a third feature set.
• A machine-learning model predicts the health outcome based on the feature sets.
• The predicted health outcome is reported.

Potential Applications

This technology could be used in healthcare settings to monitor and predict patient health outcomes in real-time, allowing for early intervention and personalized treatment plans.

Problems Solved

This technology helps in early detection of health issues, personalized patient care, and improved patient outcomes by analyzing multiple data sources simultaneously.

Benefits

The benefits of this technology include improved patient care, early detection of health issues, personalized treatment plans, and better overall health outcomes for patients.

Potential Commercial Applications

A potential commercial application for this technology could be in telemedicine platforms, remote patient monitoring systems, and healthcare analytics companies.

Possible Prior Art

One possible prior art for this technology could be similar machine learning models used in healthcare for predictive analytics and patient monitoring.

How does the machine learning model handle different types of data inputs (video, audio, text) to predict the health outcome of the patient?

The machine learning model likely uses feature extraction techniques to process and analyze the different types of data inputs and identify patterns or correlations that can help predict the health outcome of the patient.

What are the limitations or challenges of using a machine learning model on video, audio, and text data for predicting health outcomes?

Some potential limitations or challenges could include data privacy concerns, data quality issues, interpretability of the model's predictions, and the need for continuous model training and validation to ensure accuracy and reliability in predicting health outcomes.

Original Abstract Submitted

Apparatus and associated methods relate to predicting a health outcome of a patient by a machine learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is then reported.