Invention Grant
- Patent Title: Interpretable deep learning framework for mining and predictive modeling of health care data
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Application No.: US15829768Application Date: 2017-12-01
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Publication No.: US11144825B2Publication Date: 2021-10-12
- Inventor: Yan Liu , Zhengping Che , Sanjay Purushotham
- Applicant: University of Southern California
- Applicant Address: US CA Los Angeles
- Assignee: University of Southern California
- Current Assignee: University of Southern California
- Current Assignee Address: US CA Los Angeles
- Agency: Snell & Wilmer LLP
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G16H50/20 ; G06N3/04 ; G16H50/70 ; G06N5/02

Abstract:
A method for creating an interpretable model for healthcare predictions includes training, by a deep learning processor, a neural network to predict health information by providing training data, including multiple combinations of measured or observed health metrics and corresponding medical results, to the neural network. The method also includes determining, by the deep learning processor and using the neural network, prediction data including predicted results for the measured or observed health metrics for each of the multiple combinations of the measured or observed health metrics based on the training data. The method also includes training, by the deep learning processor or a learning processor, an interpretable machine learning model to make similar predictions as the neural network by providing mimic data, including combinations of the measured or observed health metrics and corresponding predicted results of the prediction data, to the interpretable machine learning model.
Public/Granted literature
- US20180158552A1 INTERPRETABLE DEEP LEARNING FRAMEWORK FOR MINING AND PREDICTIVE MODELING OF HEALTH CARE DATA Public/Granted day:2018-06-07
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