Back to search

Article

dynaPhenoM: Dynamic Phenotype Modeling from Longitudinal Patient Records Using Machine Learning

2021-11-02

Abstract excerpt

Identification of clinically meaningful subphenotypes of disease progression can facilitate better understanding of disease heterogeneity and underlying pathophysiology. We propose a machine learning algorithm, termed dynaPhenoM, to achieve this goal based on longitudinal patient records such as electronic health records (EHR) or insurance claims. Specifically, dynaPhenoM first learns a set of coherent clinical to...

Topics

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

Identifiers and source

Literature Corpus work
1df1e9b1-502b-50d7-a31a-6b288a84cc3d
DOI
10.1101/2021.11.01.21265725
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.
dynaPhenoM: Dynamic Phenotype Modeling from Longitudinal Patient Records Using Machine LearningDOI 10.1101/2021.11.01.21265725
Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.Semantic proximity
Outcome-Oriented Deep Temporal Phenotyping of Disease Progression.Semantic proximity
Machine learning on longitudinal multi-modal data enabling the understanding and prognosis of Alzheimer’s disease progressionSemantic proximity
Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.Semantic proximity
Revealing trajectories of multi-modal voxel-level changes in neurodegenerative diseases using latent event mappingSemantic proximity
Age-dependent topic modelling of comorbidities in UK Biobank identifies disease subtypes with differential genetic riskSemantic proximity
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record.Semantic proximity
An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease.Semantic proximity
MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health recordSemantic proximity
Genetic Associations with Temporal Modeling of Alzheimer’s Disease Progression Supports a Novel Paradigm for Disease RiskSemantic proximity
ALADYNOULLI: A Bayesian approach to disease progression modeling for genomic discovery and clinical predictionSemantic proximity
Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysisSemantic proximity
Select a neighboring publication to make it the new centre.