Back to search

Article

Phenotyping to predict 12-month health outcomes of older general medicine patients

2024-11-11

Abstract excerpt

<title>Abstract</title> <p><bold>Background:</bold> A variety of unsupervised learning algorithms have been used to phenotype older patients, enabling directed care and personalised treatment plans. However, the ability of the clusters to accurately discriminate for the risk of older patients, may vary depending on the methods employed. <bold>Aims: </bold>To compare seven clustering algorithms in their ability to...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
181737bb-422d-5651-bd77-601a56dfaf44
DOI
10.21203/rs.3.rs-5314625/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Phenotyping to predict 12-month health outcomes of older general medicine patientsDOI 10.21203/rs.3.rs-5314625/v1
Identifying High-Risk Patient Clusters for Falls Using Unsupervised Machine Learning on Linked Primary and Secondary Care Electronic Health Record DataSemantic proximity
Defining Aging Phenotypes and Related Outcomes: Clues to Recognize Frailty in Hospitalized Older Patients.Semantic proximity
Can Unsupervised Machine Learning Support Malnutrition Risk Screening in Older Adults? A Preliminary Study Using Multidimensional Phenotyping and Hierarchical Clustering on Principal Components.Semantic proximity
A next-generation electronic frailty index leveraging deep learning on unstructured health records extends risk prediction across the full frailty spectrumSemantic proximity
Clinical clusters during acute illness in older frailer patients with long-term mortality predictive and inferential fidelitySemantic proximity
Study protocol to establish a prospective cohort for the study of phenotypic clusters, progression pathways, and outcomes of frailty and dependence: The CohorFES.Semantic proximity
Study protocol to stablish a prospective cohort for the study of phenotypic clusters, progression paths and outcomes of frailty and dependence: The CohorFESSemantic proximity
Frailty in older adults admitted to hospital: outcomes from the Western Sydney Clinical Frailty Registry.Semantic proximity
Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity
Frailty trajectories and the prognostic value of two-stage PRISMA-7 followed by Clinical Frailty Scale screening for mortality in community- dwelling older adults: a longitudinal general-practice study in South Tyrol, ItalySemantic proximity
Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.Semantic proximity
Development and internal validation of a machine learning–based prediction model and simplified screening score for in-hospital falls: a retrospective cohort studySemantic proximity
Select a neighboring publication to make it the new centre.