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Found 9 papers, 0 papers with code
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel.
Simpler Calibration for Survival Analysis
Therefore, X-CAL has recently been proposed for the calibration, which is supposed to be used as a regularization term in the loss function of a neural network.
Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
To enable the adoption of the ever improving AI risk prediction models in practice, we have begun to explore techniques to contextualize such models along three dimensions of interest: the patients' clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions.
Disease Progression Modeling Workbench 360
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Phenotypical Ontology Driven Framework for Multi-Task Learning
Despite the large number of patients in Electronic Health Records (EHRs), the subset of usable data for modeling outcomes of specific phenotypes are often imbalanced and of modest size.
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions.
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
Counterfactual prediction is a fundamental task in decision-making.
ATTENTIVE EXPLAINABILITY FOR PATIENT TEMPORAL EMBEDDING
Learning explainable patient temporal embeddings from observational data has mostly ignored the use of RNN architecture that excel in capturing temporal data dependencies but at the expense of explainability.
