Machine Learning

Real-World Evidence, Causal Inference, and Machine Learning

On 23 May, 2023 By admin 0 Comments

May, 2023

Abstract

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.

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Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery

On 23 May, 2023 By admin 0 Comments

May, 2023

Abstract

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.

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Reinterpreting causal discovery as the task of predicting unobserved joint statistics

On 23 May, 2023 By admin 0 Comments

May, 2023

Abstract

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.

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