Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an o...
This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recov...
The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard pract...
Journal of the American Medical Informatics Association : JAMIA
33486527
OBJECTIVE: We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical re...
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspeci...
BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN).
Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs: causal forest. In a recent paper, J...
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that...
In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimate...
Deep learning is a subfield of artificial intelligence and machine learning, based mostly on neural networks and often combined with attention algorithms, that has been used to detect and identify objects in text, audio, images, and video. Serghiou a...