Non-invasive dyspnea prediction tool using machine learning
AssigneeThe Regents of the University of California
InventorsKarapet Mkrtchyan, Erica Heinrich
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Abstract
Disclosed herein is a machine learning system to predict breathlessness, dyspnea, and respiratory discomfort in a subject through a non-invasive manner. In some embodiments, the machine learning system monitors data on the subject, such as the electrocardiography (ECG), electromyography (EMG) of the inspiratory muscles, pulse oximetry (SpO2), end-tidal and inspired partial pressures of oxygen (PO2) and carbon dioxide (PCO2), and ventilatory flow rate data.
Claims21
A computer-implemented method for determining the likelihood of a subject having dyspnea, the method comprising:
- measuring in the subject at least 1, 2, 3, 4, 5, 6, 7, or 8 of:
- BMI, gender, age, pO2, pCO2, SpO2, heart rate, and respiratory air flow;
- and
- assigning a severity score based on the at least one measurement;
- wherein the severity score exceeding a baseline value indicates the subject is likely to have dyspnea.
A system for evaluating the severity of dyspnea in a subject, the system comprising:
- a software system for measuring in the subject at least 1, 2, 3, 4, 5, 6, or 7 of:
- BMI, age, pO2, pCO2, SpO2, heart rate, and respiratory air flow;
- wherein the software system assigns a severity score based on the at least one measurement;
- wherein a higher severity score is indicative of more severe dyspnea.
A method of monitoring and treating dyspnea in a subject, the method comprising:
- measuring in the subject at least 1, 2, 3, 4, 5, 6, 7, or 8 of:
- BMI, gender, age, pO2, pCO2, SpO2, heart rate, and respiratory air flow;
- assigning a severity score based on the at least one measurement;
- and
- administering a treatment to the subject if the severity score is above a threshold value.
Dependencies are inferred from the claim text.