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.
Research
Hiroki Matsui
The University of Tokyo
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0004-4743
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: To develop and validate deep learning–based prediction models for in-hospital mortality of acute-care patients.
Methods: The main model was developed using only administrative claims data (age, sex, diagnoses, and procedures on the day of admission). We also constructed disease-specific models for acute myocardial infarction, heart failure, stroke, or pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 2010 to March 2017, we identified 46,665,933 inpatients and divided them into derivation and validation cohorts in a ratio of 95:5. The main model was developed using a 9-layer deep neural network with four hidden dense layers that had 1000 nodes and were fully connected to adjacent layers. We evaluated model discrimination ability by an area under the receiver operating characteristics curve and calibration ability by calibration plot.
Results: Among the eligible patients, 2,005,035 (4.3%) died. Discrimination and calibration of the models were satisfactory. The AUC of the main model in the validation cohort was 0.954 (95% confidential interval 0.9537–0.9547). The main model had higher discrimination ability than the disease-specific models.
Conclusions: Our deep learning-based model using diagnoses and procedures produced valid predictions of in-house mortality.
Keywords
Prognostic model, Deep learning, Real world data
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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.
Research
Hiroki Matsui
The University of Tokyo
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0004-4743
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 05 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Critical Care & Emergency Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: To develop and validate deep learning–based prediction models for in-hospital mortality of acute-care patients.
Methods: The main model was developed using only administrative claims data (age, sex, diagnoses, and procedures on the day of admission). We also constructed disease-specific models for acute myocardial infarction, heart failure, stroke, or pneumonia using common severity indices for these diseases. Using the Japanese Diagnosis Procedure Combination data from July 2010 to March 2017, we identified 46,665,933 inpatients and divided them into derivation and validation cohorts in a ratio of 95:5. The main model was developed using a 9-layer deep neural network with four hidden dense layers that had 1000 nodes and were fully connected to adjacent layers. We evaluated model discrimination ability by an area under the receiver operating characteristics curve and calibration ability by calibration plot.
Results: Among the eligible patients, 2,005,035 (4.3%) died. Discrimination and calibration of the models were satisfactory. The AUC of the main model in the validation cohort was 0.954 (95% confidential interval 0.9537–0.9547). The main model had higher discrimination ability than the disease-specific models.
Conclusions: Our deep learning-based model using diagnoses and procedures produced valid predictions of in-house mortality.
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
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