ArticleActa neuropsychiatrica2023
Lexical stability of psychiatric clinical notes from electronic health records over a decade.
Article in Acta neuropsychiatrica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evolving language of pediatric anxiety in electronic health records.JAMIA open · 2026Article
- Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.Acta neuropsychiatrica · 2026Article
- A highly scalable deep learning language model for common risks prediction among psychiatric inpatients.BMC medicine · 2025Article
- Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning.JAMA psychiatry · 2025Article
- Predicting cardiovascular disease in patients with mental illness using machine learning.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
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7 authors.
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Abstract
objectiveNatural language processing (NLP) methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice - as well as the systems and databases in which clinical notes are recorded and stored - change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested.
methodsThe lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics.
resultsWe find lexical stability of the clinical notes over time, with minor deviations during the COVID-19 pandemic. Out of 2988 data points, 17 possible changepoints (corresponding to 0.6%) were detected. The majority of these were related to the discontinuation of a specific note type.
conclusionWe find lexical and syntactic stability of clinical notes from psychiatric services over time, which bodes well for the use of NLP for predictive modelling in clinical psychiatry.
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