Dynamic and locally-faithful explanation of machine learning models for precision medicine
US11657920B2 · kind B2 · utility
1Cited by
3References
20Claims
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Key dates
| Filing date | May 21, 2020 |
| Grant date | May 23, 2023 |
| Priority date | — |
| Expiry date | Jul 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.