Search Results for author:
Found 9 papers, 5 papers with code
Image registration based automated lesion correspondence pipeline for longitudinal CT data
The algorithm employs a sequential two-step pipeline: (a) Firstly, an adaptive Hungarian algorithm is used to establish correspondence among lesions within a single volumetric image series which have been annotated by multiple radiologists at a specific timepoint.
TorchSurv: A Lightweight Package for Deep Survival Analysis
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment.
Out-of-Distribution Detection and Data Drift Monitoring using Statistical Process Control
Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model.
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses
To generate evidence regarding the safety and efficacy of artificial intelligence (AI) enabled medical devices, AI models need to be evaluated on a diverse population of patient cases, some of which may not be readily available.
Is this model reliable for everyone? Testing for strong calibration
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup.
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict.
Sequential algorithmic modification with test data reuse
Each modification introduces a risk of deteriorating performance and must be validated on a test dataset.
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
In the COPD study, BLR and MarBLR dynamically combined the original model with a continually-refitted gradient boosted tree to achieve aAUCs of 0. 924 (95%CI 0. 913-0. 935) and 0. 925 (95%CI 0. 914-0. 935), compared to the static model's aAUC of 0. 904 (95%CI 0. 892-0. 916).
