Early prediction of sepsis in the ICU using machine learning: a systematic review
Background: Sepsis is among the leading causes of death in intensive care units (ICUs)
worldwide and its recognition, particularly in the early stages of the disease, remains a …
worldwide and its recognition, particularly in the early stages of the disease, remains a …
Machine learning solutions for osteoporosis—a review
J Smets, E Shevroja, T Hügle… - Journal of bone and …, 2020 - academic.oup.com
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has
been the object of extensive research. Recent advances in machine learning (ML) have …
been the object of extensive research. Recent advances in machine learning (ML) have …
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …
researchers can employ several statistical rates, accordingly to the goal of the experiment …
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
F Cabitza, A Campagner - International Journal of Medical Informatics, 2021 - Elsevier
This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …
machine learning (ML) methodologies to medical data to extract value from them and …
Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging
Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in
medical imaging. Materials and Methods Using two large publicly available radiology …
medical imaging. Materials and Methods Using two large publicly available radiology …
[HTML][HTML] Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
There is a growing demand for the uptake of modern artificial intelligence technologies
within healthcare systems. Many of these technologies exploit historical patient health data …
within healthcare systems. Many of these technologies exploit historical patient health data …
Mitigating bias in radiology machine learning: 3. Performance metrics
The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …
about bias in ML models. Bias can arise at any step of ML creation, including data handling …
Network intrusion detection based on PSO-XGBoost model
H Jiang, Z He, G Ye, H Zhang - IEEE Access, 2020 - ieeexplore.ieee.org
Network intrusion detection system (NIDS) is a commonly used tool to detect attacks and
protect networks, while one of its general limitations is the false positive issue. On the basis …
protect networks, while one of its general limitations is the false positive issue. On the basis …
An algorithm based on deep learning for predicting in‐hospital cardiac arrest
Background In‐hospital cardiac arrest is a major burden to public health, which affects
patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac …
patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac …
[HTML][HTML] A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations
Background Screening for chronic kidney disease is a challenge in community and primary
care settings, even in high-income countries. We developed an artificial intelligence deep …
care settings, even in high-income countries. We developed an artificial intelligence deep …