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Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data

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Standard

Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data. / Danielsen, Andreas A; Fenger, Morten H J; Østergaard, Søren D; Nielbo, Kristoffer L; Mors, Ole.

In: Acta Psychiatrica Scandinavica, Vol. 140, No. 2, 08.2019, p. 147-157.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Danielsen, AA, Fenger, MHJ, Østergaard, SD, Nielbo, KL & Mors, O 2019, 'Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data', Acta Psychiatrica Scandinavica, vol. 140, no. 2, pp. 147-157. https://doi.org/10.1111/acps.13061

APA

Danielsen, A. A., Fenger, M. H. J., Østergaard, S. D., Nielbo, K. L., & Mors, O. (2019). Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data. Acta Psychiatrica Scandinavica, 140(2), 147-157. https://doi.org/10.1111/acps.13061

CBE

Danielsen AA, Fenger MHJ, Østergaard SD, Nielbo KL, Mors O. 2019. Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data. Acta Psychiatrica Scandinavica. 140(2):147-157. https://doi.org/10.1111/acps.13061

MLA

Danielsen, Andreas A et al. "Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data". Acta Psychiatrica Scandinavica. 2019, 140(2). 147-157. https://doi.org/10.1111/acps.13061

Vancouver

Danielsen AA, Fenger MHJ, Østergaard SD, Nielbo KL, Mors O. Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data. Acta Psychiatrica Scandinavica. 2019 Aug;140(2):147-157. https://doi.org/10.1111/acps.13061

Author

Danielsen, Andreas A ; Fenger, Morten H J ; Østergaard, Søren D ; Nielbo, Kristoffer L ; Mors, Ole. / Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data. In: Acta Psychiatrica Scandinavica. 2019 ; Vol. 140, No. 2. pp. 147-157.

Bibtex

@article{9159d1a6c5664fa4892ffae0c865dd2e,
title = "Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data",
abstract = "OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first three days following admission could be predicted based on analysis of electronic health data available after the first hour of admission.METHODS: The dataset consisted of clinical notes from Electronic Health Records from the Central Denmark Region and data from the Danish Health Registers from patients admitted to a psychiatric department in the period from 2011 to 2015. Supervised machine learning algorithms were trained on a randomly selected subset of the data and validated using an independent test dataset.RESULTS: A total of 5,050 patients with 8,869 admissions were included in the study. One-hundred patients were mechanically restrained in the period between one hour and three days after the admission. A Random Forest algorithm predicted MR with an area under the curve of 0.87 (95% CI 0.79-0.93). At 94% specificity, the sensitivity was 56%. Among the ten strongest predictors, nine were derived from the clinical notes.CONCLUSIONS: These findings open for the development of an early warning system that may guide interventions to reduce the use of MR. This article is protected by copyright. All rights reserved.",
keywords = "coercion, electronic medical records, mental disorders, natural language processing, supervised machine learning",
author = "Danielsen, {Andreas A} and Fenger, {Morten H J} and {\O}stergaard, {S{\o}ren D} and Nielbo, {Kristoffer L} and Ole Mors",
note = "This article is protected by copyright. All rights reserved.",
year = "2019",
month = aug,
doi = "10.1111/acps.13061",
language = "English",
volume = "140",
pages = "147--157",
journal = "Acta Psychiatrica Scandinavica",
issn = "0001-690X",
publisher = "Jossey-Bass",
number = "2",

}

RIS

TY - JOUR

T1 - Predicting Mechanical Restraint of Psychiatric Inpatients by Applying Machine Learning on Electronic Health Data

AU - Danielsen, Andreas A

AU - Fenger, Morten H J

AU - Østergaard, Søren D

AU - Nielbo, Kristoffer L

AU - Mors, Ole

N1 - This article is protected by copyright. All rights reserved.

PY - 2019/8

Y1 - 2019/8

N2 - OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first three days following admission could be predicted based on analysis of electronic health data available after the first hour of admission.METHODS: The dataset consisted of clinical notes from Electronic Health Records from the Central Denmark Region and data from the Danish Health Registers from patients admitted to a psychiatric department in the period from 2011 to 2015. Supervised machine learning algorithms were trained on a randomly selected subset of the data and validated using an independent test dataset.RESULTS: A total of 5,050 patients with 8,869 admissions were included in the study. One-hundred patients were mechanically restrained in the period between one hour and three days after the admission. A Random Forest algorithm predicted MR with an area under the curve of 0.87 (95% CI 0.79-0.93). At 94% specificity, the sensitivity was 56%. Among the ten strongest predictors, nine were derived from the clinical notes.CONCLUSIONS: These findings open for the development of an early warning system that may guide interventions to reduce the use of MR. This article is protected by copyright. All rights reserved.

AB - OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this study was to investigate whether incident MR occurring in the first three days following admission could be predicted based on analysis of electronic health data available after the first hour of admission.METHODS: The dataset consisted of clinical notes from Electronic Health Records from the Central Denmark Region and data from the Danish Health Registers from patients admitted to a psychiatric department in the period from 2011 to 2015. Supervised machine learning algorithms were trained on a randomly selected subset of the data and validated using an independent test dataset.RESULTS: A total of 5,050 patients with 8,869 admissions were included in the study. One-hundred patients were mechanically restrained in the period between one hour and three days after the admission. A Random Forest algorithm predicted MR with an area under the curve of 0.87 (95% CI 0.79-0.93). At 94% specificity, the sensitivity was 56%. Among the ten strongest predictors, nine were derived from the clinical notes.CONCLUSIONS: These findings open for the development of an early warning system that may guide interventions to reduce the use of MR. This article is protected by copyright. All rights reserved.

KW - coercion

KW - electronic medical records

KW - mental disorders

KW - natural language processing

KW - supervised machine learning

U2 - 10.1111/acps.13061

DO - 10.1111/acps.13061

M3 - Journal article

C2 - 31209866

VL - 140

SP - 147

EP - 157

JO - Acta Psychiatrica Scandinavica

JF - Acta Psychiatrica Scandinavica

SN - 0001-690X

IS - 2

ER -