Fractional dynamics foster deep learning of medical condition prediction
AssigneesUniversity of Southern California, Politehnica University Timisoara, Victor Babes University of Medicine and Pharmacy Timisoara, COPD Foundation, Inc.
InventorsPaul Bogdan, Mingxi Cheng, Gaurav Gupta, Andrei Lihu, David Mannino, Stefan Mihaicuta, Mihai Udrescu, Lucretia Udrescu, Chenzhong Yin
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Abstract
Applicant discloses herein relate to systems, methods, apparatuses, and non-transitory computer readable media for generating physiological signals datasets, analyzing physiological signals in the physiological signals datasets, extract fractional dynamics signatures specific to Chronic Obstructive Pulmonary Disease (COPD) medical records, and identifying, using a deep neural network (DNN), a COPD stage.
Claims20
A method, comprising:
- receiving, by a processor, a plurality of physiological signals datasets comprising one or more physiological signals;
- identifying, by the processor, a first physiological signal of the physiological signals datasets;
- analyzing, by the processor, the first physiological signal of the physiological signals datasets;
- extracting, by the processor, one or more fractional dynamics signatures specific to one or more corresponding medical records;
- and
- identifying, using a deep neural network (DNN), a corresponding stage of the one or more medical records.
A system, comprising:
- a memory;
- a processor, configured to:
- receive a plurality of physiological signals datasets comprising one or more physiological signals;
- identify a first physiological signal of the physiological signals datasets;
- analyze the first physiological signal of the physiological signals datasets;
- extract one or more fractional dynamics signatures specific to one or more corresponding medical records;
- and
- identify, using a deep neural network (DNN), a corresponding stage of the one or more medical records.
A non-transitory processor-readable medium comprising processor-readable instructions, such that, when executed by a processor, causes the processor to:
- receive a plurality of physiological signals datasets comprising one or more physiological signals;
- identify a first physiological signal of the physiological signals datasets;
- analyze the first physiological signal of the physiological signals datasets;
- extract one or more fractional dynamics signatures specific to one or more corresponding medical records;
- and
- identify, using a deep neural network (DNN), a corresponding stage of the one or more medical records.
The non-transitory processor-readable medium of claim 18, wherein the processor is caused to train the DNN to identify one of a plurality of COPD stages using fractal dynamic network signatures.
Dependencies are inferred from the claim text.