[HTML][HTML] A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to …

M Safaei, EA Sundararajan, M Driss, W Boulila… - Computers in biology …, 2021 - Elsevier
Obesity is considered a principal public health concern and ranked as the fifth foremost
reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that …

The secondary use of electronic health records for data mining: Data characteristics and challenges

T Sarwar, S Seifollahi, J Chan, X Zhang… - ACM Computing …, 2022 - dl.acm.org
The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …

[HTML][HTML] Prediction of early childhood obesity with machine learning and electronic health record data

X Pang, CB Forrest, F Lê-Scherban… - International journal of …, 2021 - Elsevier
Objective This study compares seven machine learning models developed to predict
childhood obesity from age> 2 to≤ 7 years using Electronic Healthcare Record (EHR) data …

An extensive data processing pipeline for mimic-iv

M Gupta, B Gallamoza, N Cutrona… - … Learning for Health, 2022 - proceedings.mlr.press
An increasing amount of research is being devoted to applying machine learning methods to
electronic health record (EHR) data for various clinical purposes. This growing area of …

[HTML][HTML] Machine learning models to predict childhood and adolescent obesity: a review

G Colmenarejo - Nutrients, 2020 - mdpi.com
The prevalence of childhood and adolescence overweight an obesity is raising at an
alarming rate in many countries. This poses a serious threat to the current and near-future …

TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model

Y Wang, T Fu, Y Xu, Z Ma, H Xu, B Du, Y Lu… - ACM Transactions on …, 2024 - dl.acm.org
Clinical trials are indispensable for medical research and the development of new
treatments. However, clinical trials often involve thousands of participants and can span …

Missing value imputation methods for electronic health records

K Psychogyios, L Ilias, C Ntanos, D Askounis - IEEE Access, 2023 - ieeexplore.ieee.org
Electronic health records (EHR) are patient-level information, eg, laboratory tests and
questionnaires, stored in electronic format. Compared to physical records, the EHR …

A comprehensive analysis of artificial intelligence techniques for the prediction and prognosis of lifestyle diseases

K Modi, I Singh, Y Kumar - Archives of Computational Methods in …, 2023 - Springer
Artificial intelligence is the fastest growing data-driven technology and is currently used in all
major fields and reduces the work of humans. Artificial intelligence can analyse extensive …

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

G Ghosheh, J Li, T Zhu - arXiv preprint arXiv:2203.07018, 2022 - arxiv.org
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …

[HTML][HTML] A systematic literature review on outlier detection in wireless sensor networks

M Safaei, S Asadi, M Driss, W Boulila, A Alsaeedi… - Symmetry, 2020 - mdpi.com
A wireless sensor network (WSN) is defined as a set of spatially distributed and
interconnected sensor nodes. WSNs allow one to monitor and recognize environmental …