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Predicting Long-Ter...
Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network : A Study Based on the Scandinavian Obesity Surgery Registry
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- Cao, Yang, Associate Professor, 1972- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Clinical Epidemiology and Biostatistics
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- Raoof, Mustafa, 1966- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Department of Surgery
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- Montgomery, Scott, 1961- (författare)
- Karolinska Institutet,Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Epidemiology and Public Health, University College London, London, UK,Clinical Epidemiology and Biostatistics
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- Ottosson, Johan, 1957- (författare)
- Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Surgery
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- Näslund, Ingmar (författare)
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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(creator_code:org_t)
- 2019-12-05
- 2019
- Engelska.
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 8:12
- Relaterad länk:
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https://doi.org/10.3...
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kirurgi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Surgery (hsv//eng)
Nyckelord
- Bariatric surgery
- conventional neural network
- deep learning
- health-related quality of life
- prediction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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