Search results for "Predictive model"
showing 10 items of 74 documents
Predicting shifting sustainability trade-offs in marine finfish aquaculture under climate change
2018
Defining sustainability goals is a crucial but difficult task because it often involves the quantification of multiple interrelated and sometimes conflicting components. This complexity may be exacerbated by climate change, which will increase environmental vulnerability in aquaculture and potentially compromise the ability to meet the needs of a growing human population. Here, we developed an approach to inform sustainable aquaculture by quantifying spatio-temporal shifts in critical trade-offs between environmental costs and benefits using the time to reach the commercial size as a possible proxy of economic implications of aquaculture under climate change. Our results indicate that optim…
Soil erosion modelling: a global review and statistical analysis
2021
40 Pags.- 10 Figs.- 2 Tabls.- Suppl. Informat. The definitive version is available at: https://www.sciencedirect.com/science/journal/00489697
Predictive model to identify the risk of losing protective sensibility of the foot in patients with diabetes mellitus
2019
Diabetic neuropathy is defined as the presence of symptoms and signs of peripheral nerve dysfunction in diabetics. The aim of this study is to develop a predictive logistic model to identify the risk of losing protective sensitivity in the foot. This descriptive cross‐sectional study included 111 patients diagnosed with diabetes mellitus. Participants completed a questionnaire designed to evaluate neuropathic symptoms, and multivariate analysis was subsequently performed to identify an optimal predictive model. The explanatory capacity was evaluated by calculating the R (2) coefficient of Nagelkerke. Predictive capacity was evaluated by calculating sensitivity, specificity, and estimation o…
A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Popu…
2021
Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models wer…
On the relationship between some production parameters and a vegetation index in viticulture
2013
The use and timing of many agronomical practices such as the scheduling of irrigation and harvesting are dependent on accurate vineyard sampling of qualitative and productive parameters. Crop forecasting also depends on the representativeness of vineyard samples during the whole phenological period. This manuscript summarizes the last two years of precision viticulture in Sicily (Italy); agronomic campaigns were carried out in 2012 and 2013 within the "Tenute Rapitalà" and "Donnafugata" farms. Normalized Difference Vegetation Index derived from satellite images (RapidEye) acquired at berry set, pre-veraison and ripening phenological stages (occurred at June, July and August respectively) ha…
A laparoscopic risk-adjusted model to predict major complications after primary debulking surgery in ovarian cancer: A single-institution assessment
2016
Abstract Objective To develop and validate a simple adjusted laparoscopic score to predict major postoperative complications after primary debulking surgery (PDS) in advanced epithelial ovarian cancer (AEOC). Methods From January 2006 to June 2015, preoperative, intraoperative, and post-operative outcome data from patients undergoing staging laparoscopy (S-LPS) before receiving PDS (n=555) were prospectively collected in an electronic database and retrospectively analyzed. Major complications were defined as levels 3 to 5 of MSKCC classification. On the basis of a multivariate regression model, the score was developed using a random two-thirds of the population (n=370) and was validated on …
Adaptive Methodology for Designing a Predictive Model of Cardiac Arrhythmia Symptoms Based on Cubic Neural Unit
2017
A cubic neural unit is a kind of a higher-order neural unit which can be used for prediction tasks among others, in the medical field. The example of the tasks includes monitoring cardiac behavior in real-time either for preemptive treatment, or for supporting a doctor to reach a more accurate diagnosis. We propose a predictive model which has been developed as an application in open source code with the aim to make it publicly accessible for research community and medical professionals and also to decrease the implementation cost. The proposed model uses sample-by-sample adaptation of the gradient descent method with error backpropagation. This paper presents an application of a cubic neur…
The use of prediction models of spontaneous pregnancy in in vitro fertilization units reveals differences between the expected results of public and …
2009
To evaluate the applicability of prediction models (PM) of spontaneous pregnancy (SP) in a population of infertile patients from a university-affiliated private assisted reproductive technology center (Instituto Valenciano de Infertilidad) and in the reproductive medicine section of a public university hospital (La Fe), both belonging to the same city (Valencia, Spain) between January and December 2008. We calculated the probability of SP using the PM developed by Hunault et al. in our two populations, and observed an estimated probability of SP40% or the PM applicable in approximately 97% of the studied couples, and statistical differences between pregnancy probabilities in the two setting…
Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.
2019
Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to…
Dangerous relationships : biases in freshwater bioassessment based on observed to expected ratios
2018
Copyright by the Ecological Society of America The ecological assessment of freshwaters is currently primarily based on biological communities and the reference condition approach (RCA). In the RCA, the communities in streams and lakes disturbed by humans are compared with communities in reference conditions with no or minimal anthropogenic influence. The currently favored rationale is using selected community metrics for which the expected values (E) for each site are typically estimated from environmental variables using a predictive model based on the reference data. The proportional differences between the observed values (O) and E are then derived, and the decision rules for status ass…