Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Discovering associations among diagnosis groups using topic modeling.
23
Zitationen
4
Autoren
2014
Jahr
Abstract
With the rapid growth of electronic medical records (EMR), there is an increasing need of automatically extract patterns or rules from EMR data with machine learning and data mining technqiues. In this work, we applied unsupervised statistical model, latent Dirichlet allocations (LDA), to cluster patient diagnoics groups from Rochester Epidemiology Projects (REP). The initial results show that LDA holds the potential for broad application in epidemiogloy as well as other biomedical studies due to its unsupervised nature and great interpretive power.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.610 Zit.
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
2005 · 10.527 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.878 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.447 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.944 Zit.