AI Medical Compendium Journal:
International journal of epidemiology

Showing 1 to 10 of 17 articles

Machine learning to assist risk-of-bias assessments in systematic reviews.

International journal of epidemiology
BACKGROUND: Risk-of-bias assessments are now a standard component of systematic reviews. At present, reviewers need to manually identify relevant parts of research articles for a set of methodological elements that affect the risk of bias, in order t...

Practical considerations for specifying a super learner.

International journal of epidemiology
Common tasks encountered in epidemiology, including disease incidence estimation and causal inference, rely on predictive modelling. Constructing a predictive model can be thought of as learning a prediction function (a function that takes as input c...

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome.

International journal of epidemiology
Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks t...

Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach.

International journal of epidemiology
BACKGROUND: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep le...

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

International journal of epidemiology
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection ...

Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning.

International journal of epidemiology
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Ne...