Emerging Role of Nicotinamide Riboside in Health and Diseases
Real-World Evidence, Causal Inference, and Machine Learning
The current focus on real world evidence (RWE) is occurring at a time when at least two major trends are converging. First, is
the progress made in observational research design and methods over the past decade. Second, the development of
numerous large observational healthcare databases around the world is creating repositories of improved data assets to
support observational research.
Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery
Objectives: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment
response to MxS can be heterogeneous. Here, we demonstrate an open-source predictive analytics platform that applies
machine-learning techniques to a common data model; we develop and validate a predictive model of antihyperglycemic
medication cessation (validated proxy for A1c control) in patients with treated T2D who underwent MxS.
Reinterpreting causal discovery as the task of predicting unobserved joint statistics
If X,Y,Z denote sets of random variables, two different data sources may contain samples from PX,Y and PY,Z, respectively. We argue that causal discovery can help inferring properties of the `unobserved joint distributions' PX,Y,Z or PX,Z. The properties may be conditional independences (as in `integrative causal inference') or also quantitative statements about dependences.
A Platform for the Biomedical Application of Large Language Models
A Causal Roadmap for Generating High-Quality Real-World Evidence
Local-to-Global Causal Reasoning for Cross-Document Relation Extraction
Bayesian learning of network structures from interventional experimental data
LeTI: Learning to Generate from Textual Interactions