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Found 9 papers, 3 papers with code
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design
Given study-specific metadata, CTBench evaluates AI models' ability to determine the baseline features of a clinical trial (CT), which include demographic and relevant features collected at the trial's start from all participants.
Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter
The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19.
Downstream Fairness Caveats with Synthetic Healthcare Data
In this paper, we evaluate the fairness of models generated on two healthcare datasets for gender and race biases.
Synthetic Event Time Series Health Data Generation
Due to the complexity of the real data, in which each patient visit is an event, we transform the data by using summary statistics to characterize the events for a fixed set of time intervals, to facilitate analysis and interpretability.
Making Study Populations Visible through Knowledge Graphs
To address these challenges, we develop an ontology-enabled prototype system, which exposes the population descriptions in research studies in a declarative manner, with the ultimate goal of allowing medical practitioners to better understand the applicability and generalizability of treatment recommendations.
Semantically-aware population health risk analyses
One primary task of population health analysis is the identification of risk factors that, for some subpopulation, have a significant association with some health condition.
A Precision Environment-Wide Association Study of Hypertension via Supervised Cadre Models
Instead, we apply the supervised cadre model (SCM), which does use this metric.
Knowledge Integration for Disease Characterization: A Breast Cancer Example
With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current.
Cadre Modeling: Simultaneously Discovering Subpopulations and Predictive Models
In a materials-by-design case study, our model provides state-of-the-art prediction of polymer glass transition temperature.
