Topics

Individualized Patient Risk Stratification Using Machine Learning and Topological Data Analysis.

08:00 EDT 13th March 2020 | BioPortfolio

Summary of "Individualized Patient Risk Stratification Using Machine Learning and Topological Data Analysis."

No Summary Available

Affiliation

Journal Details

This article was published in the following journal.

Name: JACC. Cardiovascular imaging
ISSN: 1876-7591
Pages:

Links

DeepDyve research library

PubMed Articles [26467 Associated PubMed Articles listed on BioPortfolio]

A deep learning model for pediatric patient risk stratification.

Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model-a type of machine learning that does not require human inputs-...

A new risk stratification score for patients with suspected cardiac chest pain in emergency departments, based on machine learning.

Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score.

Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac res...

A Survey of Optimization Methods From a Machine Learning Perspective.

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much att...

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.

Scoring systems are suboptimal for determining risk in patients with gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model ...

Clinical Trials [11551 Associated Clinical Trials listed on BioPortfolio]

Machine Learning-Based Risk Profile Classification of Patients Undergoing Elective Heart Valve Surgery

Machine learning methods potentially provide a highly accurate and detailed assessment of expected individual patient risk before elective cardiac surgery. Correct anticipation of this ris...

Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy

The Hamlet.rt study is a prospective data collection and patient questionnaire study for patients undergoing image-guided radiotherapy with curative intent. The aim of the study is to use...

Subpopulation-Specific Sepsis Identification Using Machine Learning

The investigators propose to develop and evaluate a hospital department-specific machine learning based clinical decision support (CDS) system for early sepsis prediction, focused on impro...

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascu...

Telemedicine Notifications With Machine Learning for Postoperative Care

The ODIN-Report study will be a randomized controlled trial of the effect of providing machine learning risk forecasts to providers caring for patients immediately after surgery on serious...

Medical and Biotech [MESH] Definitions

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.

The development and application of computational models of human pathophysiology that are individualized to patient-specific data.

Quick Search


DeepDyve research library

Searches Linking to this Article