L. Hansen, Kenneth C. Enevoldsen, M. Bernstorff, E. Perfalk, A. Danielsen, K. Nielbo, S. Østergaard
{"title":"Lexical Stability of Psychiatric Clinical Notes from Electronic Health Records over a Decade","authors":"L. Hansen, Kenneth C. Enevoldsen, M. Bernstorff, E. Perfalk, A. Danielsen, K. Nielbo, S. Østergaard","doi":"10.1101/2022.09.05.22279610","DOIUrl":null,"url":null,"abstract":"Natural Language Processing 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. Therefore, in this study, we examined the 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) by quantifying sentence length, readability, syntactic complexity and clinical content - and estimating changepoints in these metrics. We find lexical and syntactic stability over time, which bodes well for the use of Natural Language Processing for predictive modelling in clinical practice.","PeriodicalId":7066,"journal":{"name":"Acta Neuropsychiatrica","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Neuropsychiatrica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1101/2022.09.05.22279610","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 2
Abstract
Natural Language Processing 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. Therefore, in this study, we examined the 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) by quantifying sentence length, readability, syntactic complexity and clinical content - and estimating changepoints in these metrics. We find lexical and syntactic stability over time, which bodes well for the use of Natural Language Processing for predictive modelling in clinical practice.
期刊介绍:
Acta Neuropsychiatrica is an international journal focussing on translational neuropsychiatry. It publishes high-quality original research papers and reviews. The Journal''s scope specifically highlights the pathway from discovery to clinical applications, healthcare and global health that can be viewed broadly as the spectrum of work that marks the pathway from discovery to global health.