Predictive performance of machine learning compared to statistical methods in time-to-event analysis of cardiovascular disease: a systematic review protocol
Background
Globally, cardiovascular disease (CVD) remains the leading cause of death, warranting effective management and prevention measures. Risk prediction tools are indispensable for directing primary and secondary prevention strategies for CVD and are critical for estimating CVD risk. Machine learning (ML) methodologies have experienced significant advancements across numerous practical domains in recent years. Several ML and statistical models predicting CVD time-to-event outcomes have been developed. However, it is not known as to which of the two model types—ML and statistical models—have higher discrim...
Source: BMJ Open - April 15, 2024 Category: General Medicine Authors: Suliman, A., Masud, M., Serhani, M. A., Abdullahi, A. S., Oulhaj, A. Tags: Open access, Cardiovascular medicine Source Type: research
The limited value of machine learning approach to improving predictive performance: The Ministry of Justice Case Assessment Tool.
This study aims to improve the predictive performance of existing risk assessment tools and the predictive validity of the original Ministry of Justice Case Assessment Tool (MJCA) concerning recidivism rates using machine learning (ML) and examine whether the tool’s predictive performance can be improved. With follow-up data on 5,942 individuals in Japanese Juvenile Assessment Centers, the study uses ML methods, such as the K-nearest neighbor algorithm, support vector machine, random forest, gradient boosting tree, and multilayer perceptron, to improve the MJCA’s prediction power. The results show that the predictive v...
Source: Psychology, Public Policy, and Law - April 15, 2024 Category: Medical Law Source Type: research
Differentiating Ischemic Stroke Patients from Healthy Subjects Using a Large-Scale, Retrospective EEG Database and Machine Learning Methods
We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-minute resting electroencephalogram (EEG) recording from which features can be computed. (Source: Journal of Stroke and Cerebrovascular Diseases)
Source: Journal of Stroke and Cerebrovascular Diseases - April 15, 2024 Category: Neurology Authors: William Peterson, Nithya Ramakrishnan, Krag Browder, Nerses Sanossian, Peggy Nguyen, Ezekiel Fink Source Type: research
A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring
This study pres ents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts. (Source: Journal of Clinical Monitoring and Computing)
Source: Journal of Clinical Monitoring and Computing - April 15, 2024 Category: Information Technology Source Type: research
The Usefulness of YouTube Videos Related to Endoscopic Sinus Surgery for Surgical Residents
Conclusions Overall, the quality of included videos was poor. OHNS residents should not rely solely or primarily on YouTube videos to learn surgical skills relevant to ESS. To maximize potential of online teaching, high-quality videos should be used to compliment other methods of teaching. [...] Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, GermanyArticle in Thieme eJournals: Table of contents | Abstract | Full text (Source: Journal of Neurological Surgery Part B: Skull Base)
Source: Journal of Neurological Surgery Part B: Skull Base - April 15, 2024 Category: Neurosurgery Authors: Shapiro, Justin Levin, Marc Sunba, Saud Steinberg, Emily Wu, Vince Lee, John M. Tags: Original Article Source Type: research
