Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T07:24:36.888Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting involuntary admission among patients with psychotic disorder

Published online by Cambridge University Press:  01 September 2022

E. Perfalk ,
L. Hansen ,
K. Nielbo ,
A. Danielsen  and
S. Dinesen
E. Perfalk*
Affiliation:
Børglumvej 11, Department For Affective Disorders, Aarhus N, Denmark
L. Hansen
Affiliation:
Børglumvej 11, Department For Affective Disorders, Aarhus N, Denmark
K. Nielbo
Affiliation:
Aarhus University, School Of Culture And Society, Aarhus C, Denmark
A. Danielsen
Affiliation:
Børglumvej 11, Department For Affective Disorders, Aarhus N, Denmark
S. Dinesen
Affiliation:
Børglumvej 11, Department For Affective Disorders, Aarhus N, Denmark
*
*Corresponding author.

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Introduction

Involuntary admissions are increasing in numbers across Europe.1 They can be traumatic for the patients2 and are associated with large societal costs.3 Individuals with psychotic disorder are at particularly elevated risk of involuntary admission.

Objectives

This study aims to investigate whether machine learning methods including natural language processing can predict involuntary admission among patients with psychotic disorder.

Methods

We have obtained a dataset based on electronic health records for all patients having had at least one contact with the psychiatric services in the Central Denmark Region from 2011 to 2021. This dataset covers more than 120,000 patients, of which approximately 10,000 have been diagnosed with a psychotic disorder. The dataset contains both structured data, such as diagnoses, blood tests etc., as well as unstructured data (text). We will train machine learning models, basic logistic regression-models as well as state-of-the-art neural networks, to predict involuntary admission after contacts to the psychiatric services.

Results

As the machine learning models are under development, no results are available at this time. Preliminary results are expected in spring 2022.

Conclusions

If involuntary admission can be predicted among patients with psychotic disorder based on data from electronic health records, it will pave the way for potentially preventive interventions. References: 1. Sheridans-Rains, L et al., 2019 2. Frueh, B.C et al., 2005 3. Smith,S., 2020

Disclosure

No significant relationships.

Keywords

Precision PsychiatryPSYCHOTIC DISORDERSInvoluntary admission
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S227
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
Submit a response

Comments

No Comments have been published for this article.