公开(公告)号：US20230181120A1

公开(公告)日：2023-06-15

申请号：US17923958

申请日：2021-04-20

Applicant: The Trustees of Princeton University

Inventor： Shayan HASSANTABAR ,  Niraj K. JHA

IPC: A61B5/00 ,  G16H10/20 ,  G16H50/80 ,  A61B5/0205 ,  A61B5/0533

CPC classification number: A61B5/7267 ,  G16H10/20 ,  G16H50/80 ,  A61B5/0205 ,  A61B5/0533

Abstract: According to various embodiments, a machine-learning based system for coronavirus detection is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and questionnaire data from a user interface. The processors are further configured to train at least one neural network based on raw physiological data and questionnaire data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one coronavirus inference model. The processors are also configured to output a coronavirus-based decision by inputting the received physiological data and questionnaire data into the generated coronavirus inference model.

公开(公告)号：US20250078998A1

公开(公告)日：2025-03-06

申请号：US18235422

申请日：2022-02-01

Applicant: The Trustees of Princeton University

Inventor： Shayan HASSANTABAR ,  Zhao ZHANG ,  Hongxu YIN ,  Niraj K. JHA

IPC: G16H50/20 ,  G16H40/67

Abstract: According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.

公开(公告)号：US20220036150A1

公开(公告)日：2022-02-03

申请号：US17275949

申请日：2019-07-12

Applicant: The Trustees of Princeton University

Inventor： Shayan HASSANTABAR ,  Zeyu WANG ,  Niraj K. JHA

IPC: G06N3/04 ,  G06N3/08

Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset is disclosed. The method includes providing an initial neural network architecture; performing a dataset modification on the dataset, the dataset modification including reducing dimensionality of the dataset; performing a first compression step on the initial neural network architecture that results in a compressed neural network architecture, the first compression step including reducing a number of neurons in one or more layers of the initial neural network architecture based on a feature compression ratio determined by the reduced dimensionality of the dataset; and performing a second compression step on the compressed neural network architecture, the second compression step including one or more of iteratively growing connections, growing neurons, and pruning connections until a desired neural network architecture has been generated.