Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Improving Patient Cohort Identification Using Natural Language Processing
40
Zitationen
2
Autoren
2016
Jahr
Abstract
Retrieving information from structured data tables in a large database may be performed with little to no difficulty, but structured data may not always contain all that is needed to retrieve accurate information compared to narratives from clinical notes. The large volume of clinical notes, however, requires special processing to access the information contained in their unstructured format. In this case study, we present a comparison of two techniques (structured data extraction and natural language processing) and we evaluate their utility in identifying a specific patient cohort from a large clinical database.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.866 Zit.
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
2005 · 10.572 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 9.010 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.649 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.202 Zit.