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Research Article
Chonghui Guo
Institute of Systems Engineering, Dalian University of Technology, PR China
Corresponding Author
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Background and Objective: Mortality prediction is widely used to stratify patients into different risk categories and to provide prognosis evaluation. Nowadays, scoring systems, which predict mortality with some scores reflecting the severity of diseases and physiological states of patients in ICU, have been widely applied for in-hospital mortality prediction. Many research works which focus on designing better machine learning models and algorithms for mortality prediction also have achieved great performance. However, it is not enough to make post-discharge prognosis of mortality only with the aid of better models and algorithms while richer patient related information can make big differences. In this study, we propose a deep learning method considering patient diagnosis information for post-discharge prognosis of mortality. This method can help to significantly improve the performance of prediction. Further more, we propose a method of calculating disease Shapley values to evaluate the mortality risk brought by disease factors.
Methods: Deep learning models including long short-term memory (LSTM) and temporal convolutional network (TCN) are trained with patient physiological time series data and diagnosis information of different prevalence to predict post-discharge mortality risk of different time windows. Disease Shapley values to evaluate the mortality risk brought by disease factors are the weighted average of marginal contributions of diseases to patient mortality. Experiments of several post-discharge mortality prediction tasks of different time windows are conducted on the large freely accessible MIMIC-III dataset. To provide more sufficient comparison, the diagnosis information is also introduced for traditional machine learning models.
Results: In our experiment, LSTM achieves highest AUROC and the improvements of which are 8.67%, 9.68%, 13.33%, 12.32% and 12.25% with the help of diagnosis information for five post-discharge mortality prediction tasks of different time window. Several patient examples are shown to present the mortality risk brought by disease factors, of which the analysis results are in line with clinical experiences.
Conclusions: In general, our proposed method can improve performance of ICU patient post-discharge mortality prediction and help to evaluate how much do different kinds of diseases which a patient suffers from increase his mortality, thus providing support for clinical decisions.
Keywords
Intensive care units, Mortality prediction, Deep learning, Diagnosis information, Shapley values
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Posted 08 Nov, 2021
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This preprint is under consideration at Soft Computing. A preprint is 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
Chonghui Guo
Institute of Systems Engineering, Dalian University of Technology, PR China
Corresponding Author
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: Under Review
Version 1
Posted 08 Nov, 2021
No community comments so far
Reviews received
Received 05 Nov, 2021
Reviewers invited
Invitations sent on 05 Nov, 2021
Editor assigned
On 03 Nov, 2021
First submitted
On 28 Oct, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Theoretical Computer Science
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and Objective: Mortality prediction is widely used to stratify patients into different risk categories and to provide prognosis evaluation. Nowadays, scoring systems, which predict mortality with some scores reflecting the severity of diseases and physiological states of patients in ICU, have been widely applied for in-hospital mortality prediction. Many research works which focus on designing better machine learning models and algorithms for mortality prediction also have achieved great performance. However, it is not enough to make post-discharge prognosis of mortality only with the aid of better models and algorithms while richer patient related information can make big differences. In this study, we propose a deep learning method considering patient diagnosis information for post-discharge prognosis of mortality. This method can help to significantly improve the performance of prediction. Further more, we propose a method of calculating disease Shapley values to evaluate the mortality risk brought by disease factors.
Methods: Deep learning models including long short-term memory (LSTM) and temporal convolutional network (TCN) are trained with patient physiological time series data and diagnosis information of different prevalence to predict post-discharge mortality risk of different time windows. Disease Shapley values to evaluate the mortality risk brought by disease factors are the weighted average of marginal contributions of diseases to patient mortality. Experiments of several post-discharge mortality prediction tasks of different time windows are conducted on the large freely accessible MIMIC-III dataset. To provide more sufficient comparison, the diagnosis information is also introduced for traditional machine learning models.
Results: In our experiment, LSTM achieves highest AUROC and the improvements of which are 8.67%, 9.68%, 13.33%, 12.32% and 12.25% with the help of diagnosis information for five post-discharge mortality prediction tasks of different time window. Several patient examples are shown to present the mortality risk brought by disease factors, of which the analysis results are in line with clinical experiences.
Conclusions: In general, our proposed method can improve performance of ICU patient post-discharge mortality prediction and help to evaluate how much do different kinds of diseases which a patient suffers from increase his mortality, thus providing support for clinical decisions.
This preprint is available for download as a PDF.
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