Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs)

Authors

Simone ten Hoope (ORCID: https://orcid.org/0009-0005-0750-0424)
Koen Welvaars (ORCID: https://orcid.org/0000-0003-2153-3116)
Fatma Karapinar‐Çarkit (ORCID: https://orcid.org/0000-0002-3077-9663)
Kylian van Geijtenbeek
Sander van Schaik
Mellanie Klok-Everaars

Institutions

Maastricht University (NL)
OLVG (NL)

Publication Details

Journal: BMC Medical Informatics and Decision Making
Published: 2025-08-12
DOI: https://doi.org/10.1186/s12911-025-03137-x
Citations: 1
Primary Topic: Biomedical Text Mining and Ontologies
Type: article
Field-Weighted Citation Impact: 0.81

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article

Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs)

Simone ten Hoope, Koen Welvaars, Fatma Karapinar‐Çarkit, Kylian van Geijtenbeek et al.

Aug 12, 2025

1 citations

BMC Medical Informatics and Decision Making

Biomedical Text Mining and Ontologies

0.81

article

Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs)

Simone ten Hoope, Koen Welvaars, Fatma Karapinar‐Çarkit, Kylian van Geijtenbeek, Sander van Schaik, Mellanie Klok-Everaars

August 12, 2025article en

1 citations

Abstract

No abstract available for this paper.

BMC Medical Informatics and Decision MakingVol. 25(1)

Maastricht University (NL), OLVG (NL)

Openalex Percentile: Top 25%

Biomedical Text Mining and Ontologies

0.81

DOI Open Access PDF OpenAlex

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Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs) — Simone ten Hoope, Koen Welvaars, et al. · BMC Medical Informatics and Decision Making (2025) | TGRS Research Map | TGRS