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- Cao, Yang, Associate Professor, 1972-, et al.
(författare)
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Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery : A Study Based on the Scandinavian Obesity Surgery Registry
- 2020
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Ingår i: Journal of Clinical Medicine. - : MDPI. - 2077-0383. ; 9:6
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Tidskriftsartikel (refereegranskat)abstract
- Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.
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| 2. |
- Rubensdotter, Lena, 1972-
(författare)
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Alpine lake sediment archives and catchment geomorphology : causal relationships and implications for paleoenvironmental reconstructions
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Lake sediments are frequently used as archives of climate and environmental change. Minerogenic sediment variability in alpine lakes is often used to reconstruct past glacier and slope process activity. Alpine lake sediments can however have many different origins, which may induce errors in paleoenvironmental reconstructions. The aim of this project was to enhance the understanding of minerogenic lake sedimentation in alpine lakes and improve their use as environmental archives.Catchment geomorphology and Holocene sediment sequences were analysed for five alpine lakes. Several minerogenic sediment sources were detected in catchments and sediment sequences. Slope-, fluvial-, periglacial-, nival- and aeolian sediment transportation processes contribute to create complex lake sediment patterns. Large variations in sedimentation rates were discovered within and between lakes, which has implications for sampling strategies and age-model constructions. Similar fine-grained minerogenic laminations were found in four of the investigated lakes, despite large differences in setting. The demonstrated similarity between glacial and non-glacial lakes may complicate interpretations of glaciolacustrine sediment signals.The main conclusion is that lake sedimentation in alpine environments is highly dependent on several geomorphological factors. All lakes should therefore be viewed as unique and the geomorphology should be thoroughly investigated before environmental reconstructions are based on lake sediment proxies. This study has confirmed the multi-source origin of alpine lake sediment, which also opens possibilities of more multi-faceted paleoenvironmental studies. Different process-proxies could potentially be used to separate different climate signals, e.g. precipitation, temperature and wind, in lake sediments. Analysis of grain-size distribution, detailed mineralogy and magnetic mineralogy in combination with X-ray radiography are suggested methods for such reconstructions.
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