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Research article
Jan Wolff
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2750-0606
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.
Methods: The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals.
Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping.
Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.
Keywords
Psychiatry, Hospitals, Decision Support Techniques, Machine Learning, Health Services Administration
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CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
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Editorial decision: Accept
On 31 Jan, 2020
Editor assigned
On 30 Jan, 2020
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On 29 Jan, 2020
Editor invited
On 29 Jan, 2020
Version 3
Posted 20 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 28 Jan, 2020
Review #1 received
Received 24 Jan, 2020
Reviewers invited
Invitations sent on 21 Jan, 2020
Reviewer #1 agreed
On 21 Jan, 2020
Editor assigned
On 17 Jan, 2020
Submission checks complete
On 16 Jan, 2020
Editor invited
On 16 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 23 Dec, 2019
Review #2 received
Received 16 Nov, 2019
Review #1 received
Received 16 Nov, 2019
Reviewer #2 agreed
On 12 Nov, 2019
Reviewer #1 agreed
On 12 Nov, 2019
Reviewers invited
Invitations sent on 11 Nov, 2019
Editor assigned
On 01 Nov, 2019
Submission checks complete
On 31 Oct, 2019
Editor invited
On 31 Oct, 2019
No comments provided
Editorial decision: Major revision
On 22 Oct, 2019
Review #2 received
Received 09 Oct, 2019
Review #1 received
Received 09 Oct, 2019
Reviewer #1 agreed
On 08 Oct, 2019
Reviewer #2 agreed
On 08 Oct, 2019
Reviewers invited
Invitations sent on 07 Oct, 2019
Submission checks complete
On 25 Sep, 2019
Editor assigned
On 22 Sep, 2019
Editor invited
On 21 Sep, 2019
First submitted
On 18 Sep, 2019
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PDF Downloads: ...
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Subject Areas
Medical Informatics
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A new preprint design is coming!
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Jan Wolff
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2750-0606
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
No community comments so far
Editorial decision: Accept
On 31 Jan, 2020
Editor assigned
On 30 Jan, 2020
Submission checks complete
On 29 Jan, 2020
Editor invited
On 29 Jan, 2020
Version 3
Posted 20 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 28 Jan, 2020
Review #1 received
Received 24 Jan, 2020
Reviewers invited
Invitations sent on 21 Jan, 2020
Reviewer #1 agreed
On 21 Jan, 2020
Editor assigned
On 17 Jan, 2020
Submission checks complete
On 16 Jan, 2020
Editor invited
On 16 Jan, 2020
No comments provided
Editorial decision: Minor revision
On 23 Dec, 2019
Review #2 received
Received 16 Nov, 2019
Review #1 received
Received 16 Nov, 2019
Reviewer #2 agreed
On 12 Nov, 2019
Reviewer #1 agreed
On 12 Nov, 2019
Reviewers invited
Invitations sent on 11 Nov, 2019
Editor assigned
On 01 Nov, 2019
Submission checks complete
On 31 Oct, 2019
Editor invited
On 31 Oct, 2019
No comments provided
Editorial decision: Major revision
On 22 Oct, 2019
Review #2 received
Received 09 Oct, 2019
Review #1 received
Received 09 Oct, 2019
Reviewer #1 agreed
On 08 Oct, 2019
Reviewer #2 agreed
On 08 Oct, 2019
Reviewers invited
Invitations sent on 07 Oct, 2019
Submission checks complete
On 25 Sep, 2019
Editor assigned
On 22 Sep, 2019
Editor invited
On 21 Sep, 2019
First submitted
On 18 Sep, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.
Methods: The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals.
Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping.
Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.
Figure 1
Figure 2
Figure 3
Figure 4
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