Encyclopedia of Database Systems

Jensen, Christian S.; Snodgrass, Richard T.

doi:10.1007/978-1-4899-7993-3

Cited by 41 publications

(5 citation statements)

References 6 publications

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“…In other words, it is a statistical validation technique used to assess how well an interpolation model performs [85]. Different methods are available in literature for CV application [86][87][88][89][90]. Particularly, the leave-one-out (LOO) method is adopted, removing each point in turn from the dataset and using the other points to estimate a value at the location of the removed point [91].…”

Section: Model Construction and Global Accuracy Evaluationmentioning

confidence: 99%

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

2022

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In relation to 3D bathymetric modelling, this article aims to analyze the performance of Kriging approaches in dependence of the location and density of the measured depth points. The experiments were carried out on a multi-beam sonar (MBS) dataset that includes 240,000 soundings covering a sea-bottom area near Giglio Island (Italy). Seven subsets were derived in random way from the initial regular MBS dataset, selecting an increasing number of points uniformly spaced. Seven models were generated for both Ordinary Kriging and Universal Kriging. Each model was submitted to leave-one-out cross-validation to define the exactness of the predictive values and compared with the initial grid to better evaluate the accuracy in dependence of the point number and dissemination. To investigate this relationship, a new index called MVI (Morphological Variation Index) was introduced as a measurement of the level of variation of seabed morphology. The results validate the efficiency of the Kriging methods and remark the influence of the dataset distribution on the 3D model, highlighting MVI as a useful index to represent the seabed variation as a unique value. Finally, in no rugged areas using 1 point every 1000 m2, the RMSE of the differences between measured and interpolated values falls below 1 m, while a further increment of soundings is required in the presence of a high level of variation of seabed morphology.

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Section: Model Construction and Global Accuracy Evaluationmentioning

confidence: 99%

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

2022

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“…This suggests a promising role for deep learning in efficiently managing population health and HF costs. Our main evaluation method for preventable hospitalizations and ED visits was precision at k. This method was adapted from the field of information retrieval 38 , where web search engines are a common use case. In web search, precision at k evaluates search results and corresponds to the proportion of relevant results among the top k percentile, as search engine users are mostly interested in the topmost retrieved results.…”

Section: Discussionmentioning

confidence: 99%

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

Lewis¹,

Elad²,

Beladev³

et al. 2021

Sci Rep

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Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.

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“…LOOCV helps to mitigate this risk by repeatedly training the model on slightly different subsets of the data, allowing for a more robust evaluation of its performance. LOOCV also ensures the best possible use of the dataset (i.e., 100% of the dataset is used as training data and 100% as test data) [ 22 ]. We performed LOOCV to select the optimal feature set and evaluate the model performance.…”

Section: Methodsmentioning

confidence: 99%

Using machine learning to design a short test from a full-length test of functional health literacy in adults—The development of a short form of the Danish TOFHLA

Hæsum¹,

Cichosz²,

Hejlesen³

2023

PLoS ONE

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Introduction Patients are compelled to become more involved in shared decision making with healthcare professionals in the self-management of chronic disease and general adherence to treatment. Therefore, it is valuable to be able to identify patients with low functional health literacy so they can be given special instructions about the management of chronic disease and medications. However, time spent by both patients and clinicians is a concern when introducing a screening instrument in the clinical setting, which raises the need for short instruments for assessing health literacy that can be used by patients without the involvement of healthcare personnel. This paper describes the development of a short version of the full-length Danish TOFHLA (DS-TOFHLA) that is easily applicable in the clinical context and where the use does not require a trained interviewer. Materials and methods Data were collected as a part of a large-scale telehomecare project (TeleCare North), which was a randomized controlled trial that included 1225 patients with chronic obstructive pulmonary disease. The DS-TOFHLA was developed solely using an algorithm-based selection of variables and multiple linear regression. A multiple linear regression model was developed using an exhaustive search strategy. Results The exhaustive search showed that the number of items in the full-length TOFHLA could be reduced from 17 numeracy items and 50 reading comprehension items to 20 reading comprehension items while maintaining a correlation of r = 0.90 between the scores from full-length and short versions. A generic model-based approach was developed, which is suitable for development of short versions of the TOFHLA in other languages, including the original American version. Conclusions This study demonstrated how a generic model-based approach could be applied in the development of a short version of the TOFHLA, thereby reducing the 67 items to 20 items in the short version. Furthermore, this study showed that the inclusion of numeracy items was not necessary. The development of the DS-TOFHLA presents an opportunity to reliably identify patients with inadequate functional health literacy in approximately 5 minutes without involvement of healthcare personnel. The approach may be used in the development of short versions of any scaling questionnaire.

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Encyclopedia of Database Systems

Cited by 41 publications

References 6 publications

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

The Influence of Interpolated Point Location and Density on 3D Bathymetric Models Generated by Kriging Methods: An Application on the Giglio Island Seabed (Italy)

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

Using machine learning to design a short test from a full-length test of functional health literacy in adults—The development of a short form of the Danish TOFHLA

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