bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive Analytics in Healthcare: A Hybrid Model for Efficient Medical Cost Estimation for Personalized Insurance Pricing
Authors: Bhargavi R. , Arumugam Subramanian 
Published: August 2024

Abstract

Predictive analytics in healthcare enables personalized insurance pricing by analyzing diverse datasets, including patient histories and treatment outcomes. In the recent years, Machine Learning (ML) models and statistical approaches are used to enhance predictive analytics in healthcare by accurately identifying individual risk factors and forecasting medical costs from complex datasets, enabling more personalized insurance pricing. However, these models are not adept in capturing the intricate relationships between linear and non-linear patterns within healthcare data. This limitation can restrict their effectiveness in personalizing insurance pricing to the fullest extent, underscoring the need for innovative solutions that can bridge this gap. This paper presents the Linear Regression-Gaussian Deep Belief Network (LR-GDBN), a hybrid predictive model developed to improve healthcare analytics by accurately forecasting medical costs. This model effectively captures linear and non-linear patterns in healthcare data, surpassing traditional methods. LR-GDBN is evaluated with a standard dataset, and it demonstrates a low prediction error of 0.4926 surpassing conventional statistical and machine learning models. Unlike the state-of-the-art models capable of handling only non-linear health data in insurance pricing, this model featuring ability to handle linear and non-linear data is unique of its kind in the domain of healthcare underwriting.
The Effect of obesity on dental caries: A mendelian randomization study
Authors: Tan Li , Wang Mei , Liu Qiong , Chen Yun , Zhao Ya-Qiong , Zhao Jie , Aimee Dusenge Marie , Feng Yao , Ye Qin , Hu Jing , Ou-Yang Ze-Yue , Guo Yue , Feng Yun-Zhi 
Published: August 2024

Abstract

Background
Clinical and retrospective studies suggest obesity increases the risk of dental caries, but causal mechanisms remain unclear. We employed the Mendelian randomization (MR) method to explore obesity's potential causal link to dental caries.
Methods
We extracted body mass index (BMI) and waist circumference (WC) data from the IEU OpenGWAS project database. These data were used to identify single-nucleotide polymorphisms (SNPs) associated with obesity as instrumental variables. Additionally, dental caries data were extracted from the same database. Using the R package ‘TwoSampleMR’, we conducted inverse-variance weighted (IVW) analyses and various sensitivity analyses to assess the impact of obesity on dental caries.
Results
The IVW analysis indicated that every 1-SD increase in BMI was associated with a higher incidence of dental caries (odds ratio [OR] = 1.286; 95% confidence interval [CI] = 1.122–1.474; P = 2.94E-04), as was every 1-SD increase in WC (OR = 1.242; 95% CI = 1.032–1.496; P = 2.20E-02). These findings were consistent across additional MR sensitivity analyses.
Conclusion
Our results provide evidence of a probable causal relationship between genetic susceptibility to obesity and an increased risk of dental caries. Therefore, individuals with obesity should take preventive measures to reduce the risk of dental caries.
Abstracting General Syntax for XAI after Decomposing Explanation Sub-Components
Authors: Wormald Stephen , Maldaner Matheus Kunzler , O'Connor Kristian D. , Dizon-Paradis Olivia P. , Woodard Damon L. 
Published: August 2024

Abstract

Policy makers, healthcare providers, and defense contractors need to understand many types of machine learning model behaviors. While eXplainable Artificial Intelligence (XAI) provides tools for interpreting these behaviors, few frameworks, surveys, and taxonomies produce succinct yet general notation to help researchers and practitioners describe their explainability needs and quantify whether these needs are met. Such quantified comparisons could help individuals rank XAI methods by their relevance to use-cases, select explanations best suited for individual users, and evaluate what explanations are most useful for describing model behaviors. This paper collects, decomposes, and abstracts subcomponents of common XAI methods to identify a mathematically grounded syntax that applies generally to describing modern and future explanation types while remaining useful for discovering novel XAI methods. The resulting syntax, introduced as the Qi-Framework, generally defines explanation types in terms of the information being explained, their utility to inspectors, and the methods and information used to produce explanations. Just as programming languages define syntax to structure, simplify, and standardize software development, so too the Qi-Framework acts as a common language to help researchers and practitioners select, compare, and discover XAI methods. Derivative works may extend and implement the Qi-Framework to develop a more rigorous science for interpretable machine learning and inspire collaborative competition arcoss XAI research.
Alignment of RNA Secondary Structures with Arbitrary Pseudoknots using Structural Sequences
Authors: Tesei Luca , Levi Francesca , Quadrini Michela , Merelli Emanuela 
Published: August 2024

Abstract

Background. RNA secondary structure comparison is important for RNA classification, RNA structure prediction and for the study of evolution. Comparison can be done efficiently only for pseudoknot-free structures, while alignment of pseudoknots is more computationally challenging.
Results. In this work, we start from the already introduced idea of representing RNA secondary structures with arbitrary pseudoknots using structural trees and introduce an alternative representation using sequences of integers. Each structure is associated with a unique structural sequence by identifying the way in which arcs interact in the arc diagram of the structure. Using a variant of classical edit distance, we define a new measure of dissimilarity called SERNA distance between abstractions of RNA secondary structures in which sequence is neglected and unpaired nucleotides are collapsed together to form a context.SERNA distance is then shown to be a metric, and the SERNAlign tool, based on dynamic programming, is introduced to calculate the distance and align structural sequences. SERNAling is shown to be more efficient than the tool using the structural tree approach. The level of abstraction of structural sequences is also more precise than the one of structural trees. Finally, we apply SERNA distance and agglomerative clustering to reconstruct the phylogeny of a dataset of ribosomal RNAs. We show that the performance of the reconstruction is similar to that obtained using the distance based on structural trees.
Conclusions. Structural sequences and the associated SERNA distance allow one to compare the secondary structures of the RNA with arbitrary pseudo-knots efficiently ($O(n^2$)). From a biological point of view, the distance reflects in a good way the phylogenetic classification of a curated taxonomy. The results on taxa reconstruction are similar to (or better than) the ones obtained using other tools in the literature that are able to compare pseudoknotted structures.
physical activity Reduces the Risk of Metabolic Syndrome and Circadian Syndrome - A Cross- Sectional Study from the NHANES Database 2007-2018
Authors: Lv XiaoHeng , Zhu XiaoYing 
Published: August 2024

Abstract

Background
Circadian syndrome (CircS) and metabolic syndrome (MetS) are closely associated with cardiovascular disease. physical activity has been proved to ameliorate the MetS, however, its association with CircS, as well as CircS comorbid MetS, is largely obscured.
Objective
To investigate the association between physical activity on MetS, CircS, and their comorbidity.
Methods
The associations and dose-response relationships between physical activity and MetS, CircS, and their comorbidity were analyzed using univariate and multivariate logistic regression, restricted cubic spline (RCS), and subgroup analyses.
Results
After adjusting for confounders, higher levels of physical activity were significantly associated with the risks of MetS, CircS, and the comorbidity (0.87 (0.77 ~ 0.99), p = 0.034; 0.85 (0.74 ~ 0.98), p = 0.027; and 0.87 (0.75 ~ 0.98), p = 0.026).The results of RCS suggested that MetS risk decreases with the increase of physical activity (p for overall < 0.001, p for nonlinear < 0.001), whereas CircS, and its comorbidity of MetS, shown a nonlinear trend from decreasing to increasing (p for overall < 0.001, p for nonlinear < 0.001), with the increasing physical activity. Subgroup analyses showed that increased physical activity was associated with a decreased risk of CircS and comorbidity in middle-aged and older adults, women, higher education population, low-income population, the non-obese, non-smokers, and non-drinkers.
Conclusion
physical activity could ameliorate MetS, CircS, and their comorbidity, especially for the middle-aged and married people as well as those with higher education, which provides a reference for early prevention of metabolism-related diseases in specific populations.
Incidence and predictors of lost to follow-up among women under option B+ PMTCT program at Debre Berhan town, Amhara, Ethiopia 2024: a retrospective follow-up study
Authors: Denekew Degefaw , Hailu Awraris Hailu , Workie Addisalem , Nigusie Abebe , Guday Esubalew 
Published: August 2024

Abstract

Background
Option B + is an advanced strategy in the Prevention of Mother-to-Child Transmission (PMTCT) recommended by the World Health Organization to enhance the care for HIV-positive pregnant and lactating women. Lost to follow-up in the Prevention of Mother-to-Child Transmission service poses a significant public health challenge. Yet, there is limited understanding of the incidence and predictors influencing LTFU among HIV-infected women under the Option B + PMTCT program.
Methods
A retrospective follow-up study was conducted at various health institutions in Debre Birehan town from March 11 to April 11, 2024. Simple random sampling was used to select 280 HIV-infected Pregnant and lactating women under option B + PMTCT service. Data were extracted from the PMTCT registers and individual medical records using a checklist, and linked together for analysis. The patients' characteristics were analyzed in terms of frequency and percentage. The cumulative survival probability of loss to follow-up was determined using the Kaplan-Meier survivor estimator and predictors influencing LTFU were identified using the Cox regression model. All potential predictors with a p-value of < 0.05 at a 95% confidence interval were declared statistically significant and presented as Adjusted Hazard Ratios.
Result
The cumulative incidence rate of loss to follow-up (LTFU) was found to be 8.04 (95% CI 5.6–11.3) cases per 1000 person-months with restricted mean survival time of 25.14 (95% CI: 24.25–26.04) months. The median follow-up time of the follow-up period was 16 (IQR 10–19) months. Lower level of formal education (AHR 9.8, 95% CI 1.2–21), non-disclosure of HIV status (AHR 3.0, 95% CI 1.6–12.4), and fair antiretroviral drug adherence (AHR 3.0, 95% CI 1.6–12.4) were significantly associated with lost to follow-up among women.
Conclusions and recommendations:
The overall incidence rate of lost to follow-up is higher than the national and World Health Organization target. Lost to follow-up was found to be influenced by educational status, disclosure of HIV status, and level of medication adherence.
Enhanced Fine-Grained Visual Classification through Lightweight Transformer Integration and Auxiliary Information Fusion
Authors: Zhu Zhenyang , Li Li , He Ketai 
Published: August 2024

Abstract

Fine-grained visual classification (FGVC) involves classifying multiple subcat-egories within a unified major category, a task characterized by significant intra-class variability and minimal inter-class differences. Previous methods often rely on pre-trained visual models augmented with specialized modules, typically using large scale models that are challenging for industrial deployment. Moreover , image data often comes with auxiliary information (e.g., spatiotemporal priors, attributes, and text descriptions), offering opportunities to enhance FGVC accuracy. Here we propose a novel lightweight Transformer-based approach that incorporates additional auxiliary information to enhance classification accuracy. Our method introduces a simplified pixel-focused aggregation attention (AA) to achieve local and global feature fusion and improves it with a separable aggre-gation attention (SAA) to reduce model complexity. We also present the Extra Inside Padding (EIP) method for integrating auxiliary information with minimal additional parameters. Without pre-training, our model surpasses other lightweight neural networks on fine-grained datasets such as CUB-200-2011, demonstrating a significant improvement in accuracy. Our approach offers a promising direction for FGVC tasks, highlighting the effectiveness of integrating multimodal data for enhanced performance. Our source code is available at https://github.com/yang-zzy/SAA-EIP.
Social vulnerability of different dimensions and physical frailty in older Chinese adults: a national cross-sectional study
Authors: Shi Jing , Gao Chao , Tao Yongkang , Cen Yan , Zhang Luyao , Li Sainan , Li Ying , Sang Botao , Liu Xiangfei , Ma Qinan , Zeng Xuezai , Li Hui , Liu Deping 
Published: August 2024

Abstract

Backgroud
Frailty is a significant public health concern among the elderly, with social frailty closely linked to the development and progression of physical frailty. This study examines the current status and influencing factors of social frailty among older adults in China, and explores the impact of different dimensions of social frailty on the degree of physical frailty to provide insights for delaying physical frailty.
Methods
Data were sourced from the Fourth Sample Survey of the Aged Population in Urban and Rural China by the China National Committee on Aging. Physical frailty was assessed using a frailty index model, and principal component analysis determined the dimensions of social frailty. Multifactorial linear regression and logistic regression analyses were conducted to examine the relationships between various dimensions of social frailty and frailty, as well as their impacts on the degree of frailty.
Results
A total of 12,788 older adults were included in the analysis. The frailty index (FI) value was 0.14 ± 0.10, showing an exponential increase with age, and at any age, females had higher FI values than males. Principal component analysis revealed that social frailty in the elderly primarily encompasses dimensions of living conditions, social support, social participation, economic status, and living environment. Multivariate linear regression results indicated that older adults with higher degrees of frailty are more vulnerable in terms of social support, social participation, economic status, and living environment (r = 0.095, 0.198, 0.226, 0.199, all P = 0.000), while the association with living conditions was not statistically significant. Multifactorial logistic regression analysis showed that apart from the living conditions dimension, social support, social participation, economic status, and living environment are associated with an increased risk of frailty, with economic status having the most significant impact, followed by living environment, social support, or social participation.
Conclusions
Social frailty is common among the elderly, and there is a reciprocal and promotive interaction between social and physical frailty. The impacts of different dimensions of social frailty on frailty vary, suggesting that targeted interventions should be developed to address various dimensions of social frailty to mitigate its effects on the frailty of older adults.
Correlation between Antioxidant Capacity and Phenolic Compounds of Korean Red Pine (Pinus densiflora Sieb. et Zucc.) Bark Fractions under In Vitro Gastrointestinal Digestive Conditions
Authors: Choi Seung-Su , Jung Young Sung , Hwang Gyo-Ha , Lee Sanggil , Han Nam Soo , Kim Dae-Ok 
Published: August 2024

Abstract

This study investigates the correlation between phenolic compounds and antioxidant capacities of Korean red pine (Pinus densiflora Sieb. et Zucc.) bark (RPB) fractions under in vitro gastrointestinal digestion. RPB extracts were obtained using 70% (v/v) aqueous ethanol and further fractionated with n-hexane, ethyl acetate, and water. Their antioxidant capacities were evaluated using three antioxidant assays. Phenolic compounds were quantified using a liquid chromatography system to assess their stability. The antioxidant capacity of RPB extract and its fractions were found to be significantly influenced by the digestive process. Gastric digestion increased the contents of procyanidin B1 and catechin, whereas intestinal digestion degraded most of the phenolic compounds except protocatechuic acid. Principal component analysis results showed that taxifolin had the greatest impact on data variability, indicating that this polyphenol plays an important role in the antioxidant capacity of RPB. This study provides insights into the stability and bioaccessibility of phenolic compounds in RPB under simulated gastrointestinal conditions, supporting its potential use as a source of natural antioxidants in dietary supplements.
Revisiting A Simple MLP Framework for Z-axis Rotation-invariant Point Cloud Place Recognition
Authors: Wu Junwei , Liu Jiejie , Sun Mingjie , Jiang Chenru , Smith Jeremy , Lim Eng Gee , Zhang Quan 
Published: August 2024

Abstract

The objective of point cloud place recognition is to convert a point cloud into a global descriptor that can be utilized in autonomous driving applications to identify the best-matched road scene from an extensive dataset. However, capturing a point cloud from an arbitrary view by robots or self-driving vehicles often involves scene rotations, making existing deep learning-based methods susceptible to errors.To quantify this performance degradation, we introduce a novel metric: Average Recall@N under arbitrary rotations, denoted as ''R-AR@N''. To address this issue, we propose a Geometrical Transformation Module designed to convert rotation-sensitive coordinates into rotation-invariant representations. Additionally, we observe that the design of overly complex networks may not be crucial for effective point cloud analysis. In line with the straightforward architectural design of PointMLP \cite{pointmlp}, we introduce a Local Feature Transformation module that utilizes statistical representations to transform local point features within a reasonable range. This enables the network to capture diverse geometric structures and generate a robust global descriptor.Our proposed method undergoes extensive evaluation on the Oxford outdoor dataset and three in-house datasets, demonstrating an improvement of at least 2\% over previous methods on the newly proposed ''R-AR@N'' metric. Our code is available at https://github.com/jasonwjw/RI-PointMLP
ACTIVITY-BASED COSTING METHOD: DETERMINING THE UNIT COST OF HOSPITALS IN SIDOARJO REGENCY
Authors: Widiawati Widiawati , Junjunan Mochammad Ilyas 
Published: August 2024

This study aims to find out how to analyze the activity-based costing method in calculating unit costs at Sidoarjo district hospital. This study emphasizes the concept of activity-based costing in setting rates for hospital inpatient classes based on unit cost calculations. This study uses a descriptive qualitative approach research method. Data obtained by observing and documenting. Test the validity of the data using triangulation and data sources. Then the data analysis technique used in this research is qualitative analysis of interactive models from Miles and Huberman. The results of the study show that in calculating hospitalization rates using the activity-based costing method and traditional methods there are differences in the number of rates set, resulting in differences in several classes, namely Super VVIP B class and class III. Even though some classes get insurance from outside parties, there are still some classes whose rates are cheaper when calculated using traditional methods. This is due to the large number of inpatients who choose class I over other classes, due to differences in facilities. The implication of this study is the difference in the overhead costs for each product or inpatient class, in the traditional method the overhead costs for each inpatient room are only charged to one cost driver. Whereas in the activity-based costing method, overhead costs are charged to each product or inpatient room with several cost drivers.
Mechanistic understanding of dehydroxylation reaction of smectites: Insights from reactive force field (ReaxFF) molecular dynamics simulation
Authors: Gao Pengyuan , Zhang Yingchun , Liu Xiandong , Lu Xiancai 
Published: August 2024

Abstract
Smectite, a major barrier material for contaminants in Earth’s critical zone, is a layered aluminosilicate mineral with cis-vacant (cv) and trans-vacant (tv) configurations. High-temperature transformations of smectite are directly related to the smectite’s thermal reactions. However, the precise thermal reaction mechanism and thermally induced structural transition of cv and tv smectites remain debated. In this study, we systematically investigated the mechanism of thermal reactions of cv and tv smectite models using reactive force field (ReaxFF) molecular dynamics. We explored the deprotonation and the intralayer dehydration steps of cv and tv smectites at 700K and 900K. The results revealed that the dehydroxylation reaction of cv smectite exhibited more difficulty than tv smectite at 700K, while demonstrating easier at 900K. Furthermore, it is found that the dehydroxylated cv and tv smectites evolved to a consistent structure spontaneously. Our findings further confirmed that cv smectite has a higher dehydroxylation temperature than tv smectite and thus provided a theoretical basis for distinguishing cv and tv smectites using thermogravimetric analysis (TGA). Moreover, we gathered TGA data of smectites from different regions, confirming that montmorillonites have cv structure and almost all smectites from China (over 11 provinces) are cv. This study provides a molecular-level understanding of the thermal reaction mechanisms of smectites and a physical basis for further study and application of smectites.