GitHub

Fake News Detection in Python

In this project, we have used various natural language processing techniques and machine learning algorithms to classify fake news articles using sci-kit libraries from python. The data source used ...
Frontiers

Predicting vasovagal syncope during head-up tilt test: three machine learning approaches

Syncope prediction during head-up tilt testing (HUTT) remains challenging due to the complex interplay between autonomic and cardiovascular responses. This study investigates three computational ...
qualitysafety.bmj

Wisdom of patients: predicting the quality of care using aggregated patient feedback

Correspondence to Dr Alex Griffiths, London School of Economics and Political Science, Centre for Analysis of Risk and Regulation, London WC2A 2AE, UK; a.griffiths{at}lse.ac.uk Background The Care ...
The Lancet

Developing and testing AI-based voice biomarker models to detect cognitive impairment among community dwelling adults: a cross-sectional study in Japan

cResearch Institute of Nursing Care for People and Community, University of Hyogo, 13-71, Kitaoji-cho, Akashi, Hyogo, 673-8588, Japan dDepartment of Biostatistics, National Cerebral and Cardiovascular ...
GitHub

ABHI04032006shek/Cancer-Treatment-Analysis

Cancer Treatment Analysis using Python, Pandas, Data Visualization, and Logistic Regression to uncover healthcare insights and predict patient status from cancer patient data. - ABHI04032006shek/Ca ...
BMJ

Impact of COVID-19-related data drift on machine-learning prognostic models predicting 30-day opioid-related emergency department visits, hospitalisation or mortality: a ...

Objective To develop machine-learning (ML) models during the COVID-19 pandemic and adjacent time periods to evaluate the impact of data drift on model performance. Design This prognostic study used ...
Frontiers

Development and evaluation of a machine learning-based risk prediction model for enteral feeding intolerance in sepsis patients

Although some EFI prediction models have been developed, these models often rely on traditional logistic regression or nomograms, incorporating a limited number of risk factors, and are primarily ...