Overcoming data missingness for improving predictions
US12198025B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 29, 2022 |
| Grant date | Jan 14, 2025 |
| Priority date | — |
| Expiry date | Jul 29, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed herein are methods for training and deploying a predictive model for generating a prediction, e.g., patient eligibility for a CAR-T therapy. Datasets, such as open healthcare claims datasets, may be missing data. Missing data may hamper the ability to generate sufficient information needed for training a predictive model. Methods include leveraging comprehensive datasets, such as closed claims datasets, to create training examples for input into a machine learning algorithm. In various embodiments, the comprehensive dataset is modified to simulate the data missingness in the target dataset; then, the modified dataset is paired with the ground truth label derived from the comprehensive dataset to create training examples. In various embodiments, a comprehensive dataset is paired with a target dataset to create training examples. After training a predictive model on such examples, the model can be deployed to make predictions in the target dataset even in light of missing data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.