Lexical Stability of Psychiatric Clinical Notes from Electronic Health Records over a Decade

Hansen, Lasse; Enevoldsen, Kenneth; Bernstorff, Martin; Perfalk, Erik; Danielsen, Andreas A.; Nielbo, Kristoffer Laigaard; Østergaard, Søren Dinesen

doi:10.1101/2022.09.05.22279610

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…Data prior to 2013 were dropped due to data instability, primarily due to the gradual implementation of a new EHR-system in 2011. 15,16 However, data on involuntary admissions from 2012 were used to establish incidence of involuntary admissions since these data were registered via an alternative digital system and, therefore, unaffected by the implementation of the new EHR-system. 17…”

Section: Methodsmentioning

confidence: 99%

“…The study is based on data from the PSYchiatric Clinical Outcome Prediction (PSYCOP) cohort, encompassing routine clinical EHR data from all individuals with at least one contact to the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011 to November 22, 2021. 13 The dataset includes records from all service contacts to the public hospitals in the Central Denmark Region (both psychiatric and general hospitals). A service contact can be either an inpatient admission, outpatient visit, home visit or consultation by phone, and each is labelled with a timestamp and diagnosis.…”

Section: Data Sourcementioning

confidence: 99%

“…To ensure the feasibility of subsequent implementation of a predictive machine learning model potentially developed in this study, only data collected routinely as part of standard clinical practice and recorded in the EHR system were used (i.e., there was no data collection for the purpose of this study). 13…”

Section: Data Extractionmentioning

confidence: 99%

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Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data

Perfalk,

Damgaard,

Bernstorff

et al. 2024

Preprint

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Background Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Objectives To develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs). Methods EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g., diagnoses, medication, and coercive measures) and 1,134 predictors from free text using term frequency - inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the two years prior, we predicted involuntary admission 180 days ahead. XGBoost and Elastic Net regularized logistic regression models were trained on 85% of the dataset. The best performing model was tested on the remaining 15% of the data. Results The model was trained on 50,634 voluntary inpatient discharges among 17,968 unique patients. The cohort comprised 1,672 voluntary inpatient discharges followed by an involuntary admission. The XGBoost model performed best in the training phase and obtained an area under the receiver operating curve of 0.84 in the test phase. Conclusion A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.

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Section: Methodsmentioning

confidence: 99%

Section: Data Sourcementioning

confidence: 99%

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Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data

Perfalk,

Damgaard,

Bernstorff

et al. 2024

Preprint

Self Cite

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“…This means that TextDescriptives can easily be integrated into existing workflows while leveraging the efficiency and robustness of the spaCy library. The package has already been used for analysing the linguistic stability of clinical texts (Hansen et al 2022), creating features for predicting neuropsychiatric conditions (Hansen 2022), and analysing linguistic goals of primary school students (Tannert 2023).…”

mentioning

confidence: 99%

TextDescriptives: A Python package for calculating a large variety of metrics from text

Hansen¹,

Enevoldsen²

2023

Preprint

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TextDescriptives is a Python package for calculating a large variety of statistics from text. It is built on top of spaCy and can be easily integrated into existing workflows. The package has already been used for analysing the linguistic stability of clinical texts, creating features for predicting neuropsychiatric conditions, and analysing linguistic goals of primary school students. This paper describes the package and its features.

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“…This means that TextDescriptives can easily be integrated into existing workflows while leveraging the efficiency and robustness of the spaCy library. The package has already been used for analysing the linguistic stability of clinical texts (Hansen et al, 2022), creating features for predicting neuropsychiatric conditions (Hansen et al, 2023), and analysing linguistic goals of primary school students (Tannert, 2023).…”

mentioning

confidence: 99%

TextDescriptives: A Python package for calculating a large variety of metrics from text

Hansen¹,

Olsen²,

Enevoldsen³

2023

JOSS

Self Cite

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Natural language processing (NLP) tasks often require a thorough understanding and description of the corpus. Document-level metrics can be used to identify low-quality data, assess outliers, or understand differences between groups. Further, text metrics have long been used in fields such as the digital humanities where e.g. metrics of text complexity are commonly used to analyse, understand and compare text corpora. However, extracting complex metrics can be an error-prone process and is rarely rigorously tested in research implementations. This can lead to subtle differences between implementations and reduces the reproducibility of scientific results.

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Lexical Stability of Psychiatric Clinical Notes from Electronic Health Records over a Decade

Cited by 5 publications

References 30 publications

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data

TextDescriptives: A Python package for calculating a large variety of metrics from text

TextDescriptives: A Python package for calculating a large variety of metrics from text

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