PATTERN RECOGNITION SYSTEM FOR QUANTIFYING THE LIKELIHOOD OF THE CONTRIBUTION OF MULTIPLE POSSIBLE FORMS OF CHRONIC DISEASE TO PATIENT REPORTED DYSPNEA
Stephen T. ANDERSON,Dean J. MACCARTER,David M. ANDERSON,Andrew HOFMEISTER
申请号：
US16011627
公开号：
US20180296127A1
申请日:
2018.06.18
申请国别(地区):
US
年份:
2018
代理人:
摘要：
Systems and methods for quantifying the likelihood of the contribution of multiple possible forms of chronic disease to patient reported dyspnea can include the testing protocol having a flow/volume loop, performed at rest, flowed by the measurement of cardiopulmonary exercise gas exchange variables during rest, exercise and recovery as unique data sets. The data sets are analyzed using feature extraction steps to produce a pictorial image consisting of disease silos displaying the likelihood of the contribution of various chronic diseases to patient reported dyspnea. In some embodiments, the silos are split into subclass silos. In some embodiments, multiple chronic disease indexes are used to differentiate between sub-types of a particular chronic disease (e.g., differentiating WHO 1 PH from WHO 2 or WHO 3 PH). Test results are plotted serially to asses to provide feedback to the physician on the efficacy of therapy provided to the patient.
