Machine Learning Is Enabling A New Era For Precision Medicine And Pharmacogenomics

Medicine is rapidly evolving from statistical, evidence-based approaches to predictive, genotype-directed care, driven by ...

AI support tool improves clinician decisions in real-world primary care trial

A large real-world clinical trial has found that a generative AI-powered support tool used to support frontline clinicians was safe and improved the quality of clinical decision-making, but did not ...
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 ...
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 ...
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 ...
ascopubs.org

Development of a Machine Learning Algorithm to Predict Abnormalities in Serum Phosphate in a Large Oncology Cohort

This model was trained and tested on a 70%/30% split (train/test result cohort), achieving an area under the receiver operator curve on the test set of 0.866 (95% CI, 0.857 to 0.875). Assigning a ...
Scientific Research Publishing

An Analysis and Prediction of Health Insurance Costs Using Machine Learning-Based Regressor Techniques ()

1 Tata Consultancy Services, Charlotte, NC, USA. 2 Mitaja Corportaion, Woodlawn, MD, USA. 3 Adobe, Seattle, WA, USA. 4 Microsoft, Charlotte, NC, USA. 5 Ally Financial ...
Nature

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt ...
Nature

Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree ...
BMJ

Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study

Setting We studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including ...