AI system detects sudden cardiac death risk, identifying thousands more patients annually

Each year in the U.S., more than 300,000 people die from sudden cardiac arrest, a condition in which the heart's electrical ...

New algorithm identifies disease-linked changes in cells without prior training

A new algorithm could drive breakthroughs in understanding cancer, Alzheimer's disease and other potentially fatal conditions ...

Low-cost AI could transform health care logistics in low- and middle-income countries

Managing a medical supply chain in low- and middle-income countries can mean navigating a landscape prone to extreme and ...
Journal of Medical Internet Research

Time-Dynamic AI Models to Predict Quality of Life in Patients With Breast Cancer: Development and Validation Study Using the EORTC BALANCE Cohort

ML algorithms were trained using the earlier HRQoL assessment and clinical data to predict dichotomized impairments in QLQ-C30 domains at the later assessment between 2 weeks and 5 years ahead, ...
IEEE

Analyzing Interdependencies Between IoT Data Integration Capabilities and Real-Time Operational Decision-Making: A DEMATEL Approach

Abstract: The growing use of Internet of Things (IoT) technologies has helped organizations to gather huge amounts of real-time information that has disrupted the operational decision-making systems.
BMJ

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

Objectives We validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate ...
The Lancet

Development and validation of a generalisable machine learning algorithm for identifying interstitial lung disease cohorts: a retrospective cohort study

aDepartment of Medicine, University of California San Francisco, San Francisco, CA, USA bDepartment of Medicine, University of California Los Angeles, Los Angeles, CA, USA cDepartment of Medicine, ...
Frontiers

AI-assisted screening for mild cognitive impairment using routine EHR data: a Gradient Boosting approach

To develop and internally validate a machine learning (ML) model that identifies older outpatients with MCI using routine electronic health record (EHR) data. We conducted a retrospective ...
Scientific Research Publishing

Heart Disease Prediction: A Logistic Regression Approach ()

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
PNAS

Detecting gene–environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores

Polygenic risk scores (PRSs) aggregate genetic information to estimate individual predisposition to a trait. While most PRSs model the phenotypic mean, patterns of variability can also be informative ...
sciencex

Using open research practices to conduct a systematic review and meta-analysis of health care data in pancreatic cancer

Claire Price, a PhD student in health sciences, has been focusing on quantifying weight loss prior to pancreatic cancer diagnosis, to help improve early detection while embracing the principles of ...
C&EN

Metal Oxide–Metal Organic Framework Layers for Discrimination of Multiple Gases Employing Machine Learning Algorithms

Nanotechnology Research Laboratory, Research School of Chemistry, College of Science, The Australian National University, Canberra, ACT 2601, Australia School of Engineering and Technology, The ...