Method of evaluating autoimmune disease risk and treatment selection
US12224070B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 2, 2020 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Sep 28, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H20/60
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of autoimmune conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed autoimmune conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patients medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subjects data to generate a patient diagnosis probability vector, predicting the existence of autoimmune conditions. These methods are applied to diagnose gastrointestinal autoimmune disorders using celiac disease as example.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.