Search Results for author:
Found 16 papers, 3 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.
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data.
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.
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients.
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.
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes.
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.
Explicit-Blurred Memory Network for Analyzing Patient Electronic Health Records
While networks with explicit memory have been proposed recently, the discontinuities imposed by the discrete operations make such networks harder to train and require more supervision.
A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure
To validate the identified factors, we use a specialized generalized linear model (GLM) to predict the probability of renal failure for individual patients within a specified time window.
Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Specifically, we focus on deriving epidemiological characteristics of an emerging disease and the affected population from reports of illness.
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence.
Hierarchical Quickest Change Detection via Surrogates
We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection.
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach
Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media.
