Lasse Hansen, Kenneth Enevoldsen, Martin Bernstorff, Erik Perfalk, Andreas A Danielsen, Kristoffer L Nielbo, Søren D Østergaard
{"title":"十多年来电子健康记录中精神病临床记录的词汇稳定性","authors":"Lasse Hansen, Kenneth Enevoldsen, Martin Bernstorff, Erik Perfalk, Andreas A Danielsen, Kristoffer L Nielbo, Søren D Østergaard","doi":"10.1017/neu.2023.46","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Natural 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.</p><p><strong>Methods: </strong>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) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics.</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusion: </strong>We 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.</p>","PeriodicalId":7066,"journal":{"name":"Acta Neuropsychiatrica","volume":" ","pages":"1-11"},"PeriodicalIF":3.8000,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lexical stability of psychiatric clinical notes from electronic health records over a decade.\",\"authors\":\"Lasse Hansen, Kenneth Enevoldsen, Martin Bernstorff, Erik Perfalk, Andreas A Danielsen, Kristoffer L Nielbo, Søren D Østergaard\",\"doi\":\"10.1017/neu.2023.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Natural 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.</p><p><strong>Methods: </strong>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) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics.</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusion: </strong>We 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.</p>\",\"PeriodicalId\":7066,\"journal\":{\"name\":\"Acta Neuropsychiatrica\",\"volume\":\" \",\"pages\":\"1-11\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Neuropsychiatrica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1017/neu.2023.46\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Neuropsychiatrica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1017/neu.2023.46","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
Lexical stability of psychiatric clinical notes from electronic health records over a decade.
Objective: Natural 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.
Methods: 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) was assessed by quantifying sentence length, readability, syntactic complexity and clinical content. Changepoint detection models were used to estimate potential changes in these metrics.
Results: We 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.
Conclusion: We 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.
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.